Spaces:
Runtime error
Runtime error
Commit
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dd4ae0d
1
Parent(s):
53c6380
Upload 2 files
Browse files- hotshot_xl_controlnet_pipeline.py +1389 -0
- hotshot_xl_pipeline.py +996 -0
hotshot_xl_controlnet_pipeline.py
ADDED
@@ -0,0 +1,1389 @@
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1 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
# Modifications:
|
16 |
+
# Copyright 2023 Natural Synthetics Inc. All rights reserved.
|
17 |
+
# - Adapted the SDXL Controlnet Pipeline to work temporally
|
18 |
+
|
19 |
+
import inspect
|
20 |
+
import os
|
21 |
+
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
22 |
+
|
23 |
+
import numpy as np
|
24 |
+
import PIL.Image
|
25 |
+
import torch
|
26 |
+
import torch.nn.functional as F
|
27 |
+
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
|
28 |
+
|
29 |
+
from hotshot_xl import HotshotPipelineXLOutput
|
30 |
+
|
31 |
+
from diffusers.image_processor import VaeImageProcessor
|
32 |
+
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
|
33 |
+
from diffusers.models import AutoencoderKL, ControlNetModel
|
34 |
+
from diffusers.models.attention_processor import (
|
35 |
+
AttnProcessor2_0,
|
36 |
+
LoRAAttnProcessor2_0,
|
37 |
+
LoRAXFormersAttnProcessor,
|
38 |
+
XFormersAttnProcessor,
|
39 |
+
)
|
40 |
+
from diffusers.schedulers import KarrasDiffusionSchedulers
|
41 |
+
from diffusers.utils import (
|
42 |
+
is_accelerate_available,
|
43 |
+
is_accelerate_version,
|
44 |
+
logging,
|
45 |
+
replace_example_docstring,
|
46 |
+
)
|
47 |
+
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
48 |
+
from diffusers.utils.torch_utils import randn_tensor, is_compiled_module
|
49 |
+
|
50 |
+
from ..models.unet import UNet3DConditionModel
|
51 |
+
|
52 |
+
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
|
53 |
+
from einops import rearrange
|
54 |
+
from tqdm import tqdm
|
55 |
+
|
56 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
57 |
+
|
58 |
+
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
59 |
+
"""
|
60 |
+
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
|
61 |
+
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
|
62 |
+
"""
|
63 |
+
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
64 |
+
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
65 |
+
# rescale the results from guidance (fixes overexposure)
|
66 |
+
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
67 |
+
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
68 |
+
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
|
69 |
+
return noise_cfg
|
70 |
+
|
71 |
+
EXAMPLE_DOC_STRING = """
|
72 |
+
Examples:
|
73 |
+
```py
|
74 |
+
>>> import torch
|
75 |
+
>>> from hotshot_xl import HotshotPipelineXL
|
76 |
+
>>> from diffusers import ControlNetModel
|
77 |
+
|
78 |
+
>>> pipe = HotshotXLPipeline.from_pretrained(
|
79 |
+
... "hotshotco/Hotshot-XL",
|
80 |
+
... controlnet=ControlNetModel.from_pretrained("diffusers/controlnet-canny-sdxl-1.0")
|
81 |
+
... )
|
82 |
+
|
83 |
+
>>> def canny(image):
|
84 |
+
>>> image = cv2.Canny(image, 100, 200)
|
85 |
+
>>> image = image[:, :, None]
|
86 |
+
>>> image = np.concatenate([image, image, image], axis=2)
|
87 |
+
>>> return Image.fromarray(image)
|
88 |
+
|
89 |
+
>>> # assuming you have 8 keyframes in current directory...
|
90 |
+
|
91 |
+
>>> keyframes = [f"image_{i}.jpg" for i in range(8)]
|
92 |
+
>>> control_images = [canny(Image.open(fp)) for fp in keyframes]
|
93 |
+
|
94 |
+
>>> pipe = pipe.to("cuda")
|
95 |
+
|
96 |
+
>>> prompt = "a photo of an astronaut riding a horse on mars"
|
97 |
+
>>> video = pipe(prompt,
|
98 |
+
... width=672, height=384,
|
99 |
+
... original_size=(1920, 1080),
|
100 |
+
... target_size=(512, 512),
|
101 |
+
... output_type="tensor",
|
102 |
+
... controlnet_conditioning_scale=0.7,
|
103 |
+
... control_images=control_images
|
104 |
+
).video
|
105 |
+
```
|
106 |
+
"""
|
107 |
+
class HotshotXLControlNetPipeline(
|
108 |
+
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
|
109 |
+
):
|
110 |
+
r"""
|
111 |
+
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
|
112 |
+
|
113 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
114 |
+
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
115 |
+
|
116 |
+
The pipeline also inherits the following loading methods:
|
117 |
+
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
|
118 |
+
- [`loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
|
119 |
+
- [`loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
|
120 |
+
|
121 |
+
Args:
|
122 |
+
vae ([`AutoencoderKL`]):
|
123 |
+
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
|
124 |
+
text_encoder ([`~transformers.CLIPTextModel`]):
|
125 |
+
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
|
126 |
+
text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]):
|
127 |
+
Second frozen text-encoder
|
128 |
+
([laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)).
|
129 |
+
tokenizer ([`~transformers.CLIPTokenizer`]):
|
130 |
+
A `CLIPTokenizer` to tokenize text.
|
131 |
+
tokenizer_2 ([`~transformers.CLIPTokenizer`]):
|
132 |
+
A `CLIPTokenizer` to tokenize text.
|
133 |
+
unet ([`UNet3DConditionModel`]):
|
134 |
+
A `UNet3DConditionModel` to denoise the encoded image latents.
|
135 |
+
controlnet ([`ControlNetModel`] or `List[ControlNetModel]`):
|
136 |
+
Provides additional conditioning to the `unet` during the denoising process. If you set multiple
|
137 |
+
ControlNets as a list, the outputs from each ControlNet are added together to create one combined
|
138 |
+
additional conditioning.
|
139 |
+
scheduler ([`SchedulerMixin`]):
|
140 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
141 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
142 |
+
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
|
143 |
+
Whether the negative prompt embeddings should always be set to 0. Also see the config of
|
144 |
+
`stabilityai/stable-diffusion-xl-base-1-0`.
|
145 |
+
add_watermarker (`bool`, *optional*):
|
146 |
+
Whether to use the [invisible_watermark](https://github.com/ShieldMnt/invisible-watermark/) library to
|
147 |
+
watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
|
148 |
+
watermarker is used.
|
149 |
+
"""
|
150 |
+
|
151 |
+
def __init__(
|
152 |
+
self,
|
153 |
+
vae: AutoencoderKL,
|
154 |
+
text_encoder: CLIPTextModel,
|
155 |
+
text_encoder_2: CLIPTextModelWithProjection,
|
156 |
+
tokenizer: CLIPTokenizer,
|
157 |
+
tokenizer_2: CLIPTokenizer,
|
158 |
+
unet: UNet3DConditionModel,
|
159 |
+
controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
|
160 |
+
scheduler: KarrasDiffusionSchedulers,
|
161 |
+
force_zeros_for_empty_prompt: bool = True,
|
162 |
+
add_watermarker: Optional[bool] = None,
|
163 |
+
):
|
164 |
+
super().__init__()
|
165 |
+
|
166 |
+
if isinstance(controlnet, (list, tuple)):
|
167 |
+
controlnet = MultiControlNetModel(controlnet)
|
168 |
+
|
169 |
+
self.register_modules(
|
170 |
+
vae=vae,
|
171 |
+
text_encoder=text_encoder,
|
172 |
+
text_encoder_2=text_encoder_2,
|
173 |
+
tokenizer=tokenizer,
|
174 |
+
tokenizer_2=tokenizer_2,
|
175 |
+
unet=unet,
|
176 |
+
controlnet=controlnet,
|
177 |
+
scheduler=scheduler,
|
178 |
+
)
|
179 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
180 |
+
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
|
181 |
+
self.control_image_processor = VaeImageProcessor(
|
182 |
+
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
|
183 |
+
)
|
184 |
+
|
185 |
+
self.watermark = None
|
186 |
+
|
187 |
+
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
|
188 |
+
|
189 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
|
190 |
+
def enable_vae_slicing(self):
|
191 |
+
r"""
|
192 |
+
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
193 |
+
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
194 |
+
"""
|
195 |
+
self.vae.enable_slicing()
|
196 |
+
|
197 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
|
198 |
+
def disable_vae_slicing(self):
|
199 |
+
r"""
|
200 |
+
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
201 |
+
computing decoding in one step.
|
202 |
+
"""
|
203 |
+
self.vae.disable_slicing()
|
204 |
+
|
205 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
|
206 |
+
def enable_vae_tiling(self):
|
207 |
+
r"""
|
208 |
+
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
|
209 |
+
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
210 |
+
processing larger images.
|
211 |
+
"""
|
212 |
+
self.vae.enable_tiling()
|
213 |
+
|
214 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
|
215 |
+
def disable_vae_tiling(self):
|
216 |
+
r"""
|
217 |
+
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
218 |
+
computing decoding in one step.
|
219 |
+
"""
|
220 |
+
self.vae.disable_tiling()
|
221 |
+
|
222 |
+
def enable_model_cpu_offload(self, gpu_id=0):
|
223 |
+
r"""
|
224 |
+
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
|
225 |
+
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
|
226 |
+
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
|
227 |
+
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
|
228 |
+
"""
|
229 |
+
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
|
230 |
+
from accelerate import cpu_offload_with_hook
|
231 |
+
else:
|
232 |
+
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
|
233 |
+
|
234 |
+
device = torch.device(f"cuda:{gpu_id}")
|
235 |
+
|
236 |
+
if self.device.type != "cpu":
|
237 |
+
self.to("cpu", silence_dtype_warnings=True)
|
238 |
+
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
|
239 |
+
|
240 |
+
model_sequence = (
|
241 |
+
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
|
242 |
+
)
|
243 |
+
model_sequence.extend([self.unet, self.vae])
|
244 |
+
|
245 |
+
hook = None
|
246 |
+
for cpu_offloaded_model in model_sequence:
|
247 |
+
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
|
248 |
+
|
249 |
+
cpu_offload_with_hook(self.controlnet, device)
|
250 |
+
|
251 |
+
# We'll offload the last model manually.
|
252 |
+
self.final_offload_hook = hook
|
253 |
+
|
254 |
+
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
|
255 |
+
def encode_prompt(
|
256 |
+
self,
|
257 |
+
prompt: str,
|
258 |
+
prompt_2: Optional[str] = None,
|
259 |
+
device: Optional[torch.device] = None,
|
260 |
+
num_images_per_prompt: int = 1,
|
261 |
+
do_classifier_free_guidance: bool = True,
|
262 |
+
negative_prompt: Optional[str] = None,
|
263 |
+
negative_prompt_2: Optional[str] = None,
|
264 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
265 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
266 |
+
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
267 |
+
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
268 |
+
lora_scale: Optional[float] = None,
|
269 |
+
):
|
270 |
+
r"""
|
271 |
+
Encodes the prompt into text encoder hidden states.
|
272 |
+
|
273 |
+
Args:
|
274 |
+
prompt (`str` or `List[str]`, *optional*):
|
275 |
+
prompt to be encoded
|
276 |
+
prompt_2 (`str` or `List[str]`, *optional*):
|
277 |
+
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
278 |
+
used in both text-encoders
|
279 |
+
device: (`torch.device`):
|
280 |
+
torch device
|
281 |
+
num_images_per_prompt (`int`):
|
282 |
+
number of images that should be generated per prompt
|
283 |
+
do_classifier_free_guidance (`bool`):
|
284 |
+
whether to use classifier free guidance or not
|
285 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
286 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
287 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
288 |
+
less than `1`).
|
289 |
+
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
290 |
+
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
291 |
+
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
|
292 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
293 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
294 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
295 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
296 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
297 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
298 |
+
argument.
|
299 |
+
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
300 |
+
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
301 |
+
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
302 |
+
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
303 |
+
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
304 |
+
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
305 |
+
input argument.
|
306 |
+
lora_scale (`float`, *optional*):
|
307 |
+
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
308 |
+
"""
|
309 |
+
device = device or self._execution_device
|
310 |
+
|
311 |
+
# set lora scale so that monkey patched LoRA
|
312 |
+
# function of text encoder can correctly access it
|
313 |
+
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
314 |
+
self._lora_scale = lora_scale
|
315 |
+
|
316 |
+
if prompt is not None and isinstance(prompt, str):
|
317 |
+
batch_size = 1
|
318 |
+
elif prompt is not None and isinstance(prompt, list):
|
319 |
+
batch_size = len(prompt)
|
320 |
+
else:
|
321 |
+
batch_size = prompt_embeds.shape[0]
|
322 |
+
|
323 |
+
# Define tokenizers and text encoders
|
324 |
+
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
|
325 |
+
text_encoders = (
|
326 |
+
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
|
327 |
+
)
|
328 |
+
|
329 |
+
if prompt_embeds is None:
|
330 |
+
prompt_2 = prompt_2 or prompt
|
331 |
+
# textual inversion: procecss multi-vector tokens if necessary
|
332 |
+
prompt_embeds_list = []
|
333 |
+
prompts = [prompt, prompt_2]
|
334 |
+
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
|
335 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
336 |
+
prompt = self.maybe_convert_prompt(prompt, tokenizer)
|
337 |
+
|
338 |
+
text_inputs = tokenizer(
|
339 |
+
prompt,
|
340 |
+
padding="max_length",
|
341 |
+
max_length=tokenizer.model_max_length,
|
342 |
+
truncation=True,
|
343 |
+
return_tensors="pt",
|
344 |
+
)
|
345 |
+
|
346 |
+
text_input_ids = text_inputs.input_ids
|
347 |
+
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
348 |
+
|
349 |
+
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
350 |
+
text_input_ids, untruncated_ids
|
351 |
+
):
|
352 |
+
removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
|
353 |
+
logger.warning(
|
354 |
+
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
355 |
+
f" {tokenizer.model_max_length} tokens: {removed_text}"
|
356 |
+
)
|
357 |
+
|
358 |
+
prompt_embeds = text_encoder(
|
359 |
+
text_input_ids.to(device),
|
360 |
+
output_hidden_states=True,
|
361 |
+
)
|
362 |
+
|
363 |
+
# We are only ALWAYS interested in the pooled output of the final text encoder
|
364 |
+
pooled_prompt_embeds = prompt_embeds[0]
|
365 |
+
prompt_embeds = prompt_embeds.hidden_states[-2]
|
366 |
+
|
367 |
+
prompt_embeds_list.append(prompt_embeds)
|
368 |
+
|
369 |
+
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
|
370 |
+
|
371 |
+
# get unconditional embeddings for classifier free guidance
|
372 |
+
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
|
373 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
|
374 |
+
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
|
375 |
+
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
|
376 |
+
elif do_classifier_free_guidance and negative_prompt_embeds is None:
|
377 |
+
negative_prompt = negative_prompt or ""
|
378 |
+
negative_prompt_2 = negative_prompt_2 or negative_prompt
|
379 |
+
|
380 |
+
uncond_tokens: List[str]
|
381 |
+
if prompt is not None and type(prompt) is not type(negative_prompt):
|
382 |
+
raise TypeError(
|
383 |
+
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
384 |
+
f" {type(prompt)}."
|
385 |
+
)
|
386 |
+
elif isinstance(negative_prompt, str):
|
387 |
+
uncond_tokens = [negative_prompt, negative_prompt_2]
|
388 |
+
elif batch_size != len(negative_prompt):
|
389 |
+
raise ValueError(
|
390 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
391 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
392 |
+
" the batch size of `prompt`."
|
393 |
+
)
|
394 |
+
else:
|
395 |
+
uncond_tokens = [negative_prompt, negative_prompt_2]
|
396 |
+
|
397 |
+
negative_prompt_embeds_list = []
|
398 |
+
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
|
399 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
400 |
+
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
|
401 |
+
|
402 |
+
max_length = prompt_embeds.shape[1]
|
403 |
+
uncond_input = tokenizer(
|
404 |
+
negative_prompt,
|
405 |
+
padding="max_length",
|
406 |
+
max_length=max_length,
|
407 |
+
truncation=True,
|
408 |
+
return_tensors="pt",
|
409 |
+
)
|
410 |
+
|
411 |
+
negative_prompt_embeds = text_encoder(
|
412 |
+
uncond_input.input_ids.to(device),
|
413 |
+
output_hidden_states=True,
|
414 |
+
)
|
415 |
+
# We are only ALWAYS interested in the pooled output of the final text encoder
|
416 |
+
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
|
417 |
+
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
|
418 |
+
|
419 |
+
negative_prompt_embeds_list.append(negative_prompt_embeds)
|
420 |
+
|
421 |
+
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
|
422 |
+
|
423 |
+
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
424 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
425 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
426 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
427 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
428 |
+
|
429 |
+
if do_classifier_free_guidance:
|
430 |
+
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
431 |
+
seq_len = negative_prompt_embeds.shape[1]
|
432 |
+
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
433 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
434 |
+
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
435 |
+
|
436 |
+
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
437 |
+
bs_embed * num_images_per_prompt, -1
|
438 |
+
)
|
439 |
+
if do_classifier_free_guidance:
|
440 |
+
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
441 |
+
bs_embed * num_images_per_prompt, -1
|
442 |
+
)
|
443 |
+
|
444 |
+
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
|
445 |
+
|
446 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
447 |
+
def prepare_extra_step_kwargs(self, generator, eta):
|
448 |
+
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
449 |
+
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
450 |
+
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
451 |
+
# and should be between [0, 1]
|
452 |
+
|
453 |
+
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
454 |
+
extra_step_kwargs = {}
|
455 |
+
if accepts_eta:
|
456 |
+
extra_step_kwargs["eta"] = eta
|
457 |
+
|
458 |
+
# check if the scheduler accepts generator
|
459 |
+
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
460 |
+
if accepts_generator:
|
461 |
+
extra_step_kwargs["generator"] = generator
|
462 |
+
return extra_step_kwargs
|
463 |
+
|
464 |
+
def check_inputs(
|
465 |
+
self,
|
466 |
+
prompt,
|
467 |
+
prompt_2,
|
468 |
+
control_images,
|
469 |
+
video_length,
|
470 |
+
callback_steps,
|
471 |
+
negative_prompt=None,
|
472 |
+
negative_prompt_2=None,
|
473 |
+
prompt_embeds=None,
|
474 |
+
negative_prompt_embeds=None,
|
475 |
+
pooled_prompt_embeds=None,
|
476 |
+
negative_pooled_prompt_embeds=None,
|
477 |
+
controlnet_conditioning_scale=1.0,
|
478 |
+
control_guidance_start=0.0,
|
479 |
+
control_guidance_end=1.0,
|
480 |
+
):
|
481 |
+
if (callback_steps is None) or (
|
482 |
+
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
483 |
+
):
|
484 |
+
raise ValueError(
|
485 |
+
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
486 |
+
f" {type(callback_steps)}."
|
487 |
+
)
|
488 |
+
|
489 |
+
if prompt is not None and prompt_embeds is not None:
|
490 |
+
raise ValueError(
|
491 |
+
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
492 |
+
" only forward one of the two."
|
493 |
+
)
|
494 |
+
elif prompt_2 is not None and prompt_embeds is not None:
|
495 |
+
raise ValueError(
|
496 |
+
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
497 |
+
" only forward one of the two."
|
498 |
+
)
|
499 |
+
elif prompt is None and prompt_embeds is None:
|
500 |
+
raise ValueError(
|
501 |
+
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
502 |
+
)
|
503 |
+
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
504 |
+
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
505 |
+
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
|
506 |
+
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
|
507 |
+
|
508 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
509 |
+
raise ValueError(
|
510 |
+
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
511 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
512 |
+
)
|
513 |
+
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
|
514 |
+
raise ValueError(
|
515 |
+
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
|
516 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
517 |
+
)
|
518 |
+
|
519 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
520 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
521 |
+
raise ValueError(
|
522 |
+
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
523 |
+
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
524 |
+
f" {negative_prompt_embeds.shape}."
|
525 |
+
)
|
526 |
+
|
527 |
+
if prompt_embeds is not None and pooled_prompt_embeds is None:
|
528 |
+
raise ValueError(
|
529 |
+
"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`."
|
530 |
+
)
|
531 |
+
|
532 |
+
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
|
533 |
+
raise ValueError(
|
534 |
+
"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`."
|
535 |
+
)
|
536 |
+
|
537 |
+
# `prompt` needs more sophisticated handling when there are multiple
|
538 |
+
# conditionings.
|
539 |
+
if isinstance(self.controlnet, MultiControlNetModel):
|
540 |
+
if isinstance(prompt, list):
|
541 |
+
logger.warning(
|
542 |
+
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
|
543 |
+
" prompts. The conditionings will be fixed across the prompts."
|
544 |
+
)
|
545 |
+
|
546 |
+
# Check `image`
|
547 |
+
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
|
548 |
+
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
|
549 |
+
)
|
550 |
+
if (
|
551 |
+
isinstance(self.controlnet, ControlNetModel)
|
552 |
+
or is_compiled
|
553 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)
|
554 |
+
):
|
555 |
+
|
556 |
+
assert len(control_images) == video_length
|
557 |
+
# for image in control_images:
|
558 |
+
# self.check_image(image, prompt, prompt_embeds)
|
559 |
+
elif (
|
560 |
+
isinstance(self.controlnet, MultiControlNetModel)
|
561 |
+
or is_compiled
|
562 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
|
563 |
+
):
|
564 |
+
...
|
565 |
+
# todo
|
566 |
+
#
|
567 |
+
# if not isinstance(image, list):
|
568 |
+
# raise TypeError("For multiple controlnets: `image` must be type `list`")
|
569 |
+
#
|
570 |
+
# # When `image` is a nested list:
|
571 |
+
# # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
|
572 |
+
# elif any(isinstance(i, list) for i in image):
|
573 |
+
# raise ValueError("A single batch of multiple conditionings are supported at the moment.")
|
574 |
+
# elif len(image) != len(self.controlnet.nets):
|
575 |
+
# raise ValueError(
|
576 |
+
# f"For multiple controlnets: `image` must have the same length as the number of controlnets, but got {len(image)} images and {len(self.controlnet.nets)} ControlNets."
|
577 |
+
# )
|
578 |
+
#
|
579 |
+
# for image_ in image:
|
580 |
+
# self.check_image(image_, prompt, prompt_embeds)
|
581 |
+
else:
|
582 |
+
assert False
|
583 |
+
|
584 |
+
# Check `controlnet_conditioning_scale`
|
585 |
+
if (
|
586 |
+
isinstance(self.controlnet, ControlNetModel)
|
587 |
+
or is_compiled
|
588 |
+
and isinstance(self.controlnet._orig_mod, ControlNetModel)
|
589 |
+
):
|
590 |
+
if not isinstance(controlnet_conditioning_scale, float):
|
591 |
+
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
|
592 |
+
elif (
|
593 |
+
isinstance(self.controlnet, MultiControlNetModel)
|
594 |
+
or is_compiled
|
595 |
+
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
|
596 |
+
):
|
597 |
+
if isinstance(controlnet_conditioning_scale, list):
|
598 |
+
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
|
599 |
+
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
|
600 |
+
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
|
601 |
+
self.controlnet.nets
|
602 |
+
):
|
603 |
+
raise ValueError(
|
604 |
+
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
|
605 |
+
" the same length as the number of controlnets"
|
606 |
+
)
|
607 |
+
else:
|
608 |
+
assert False
|
609 |
+
|
610 |
+
if not isinstance(control_guidance_start, (tuple, list)):
|
611 |
+
control_guidance_start = [control_guidance_start]
|
612 |
+
|
613 |
+
if not isinstance(control_guidance_end, (tuple, list)):
|
614 |
+
control_guidance_end = [control_guidance_end]
|
615 |
+
|
616 |
+
if len(control_guidance_start) != len(control_guidance_end):
|
617 |
+
raise ValueError(
|
618 |
+
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
|
619 |
+
)
|
620 |
+
|
621 |
+
if isinstance(self.controlnet, MultiControlNetModel):
|
622 |
+
if len(control_guidance_start) != len(self.controlnet.nets):
|
623 |
+
raise ValueError(
|
624 |
+
f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
|
625 |
+
)
|
626 |
+
|
627 |
+
for start, end in zip(control_guidance_start, control_guidance_end):
|
628 |
+
if start >= end:
|
629 |
+
raise ValueError(
|
630 |
+
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
|
631 |
+
)
|
632 |
+
if start < 0.0:
|
633 |
+
raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
|
634 |
+
if end > 1.0:
|
635 |
+
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
|
636 |
+
|
637 |
+
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
|
638 |
+
def check_image(self, image, prompt, prompt_embeds):
|
639 |
+
image_is_pil = isinstance(image, PIL.Image.Image)
|
640 |
+
image_is_tensor = isinstance(image, torch.Tensor)
|
641 |
+
image_is_np = isinstance(image, np.ndarray)
|
642 |
+
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
|
643 |
+
image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor)
|
644 |
+
image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray)
|
645 |
+
|
646 |
+
if (
|
647 |
+
not image_is_pil
|
648 |
+
and not image_is_tensor
|
649 |
+
and not image_is_np
|
650 |
+
and not image_is_pil_list
|
651 |
+
and not image_is_tensor_list
|
652 |
+
and not image_is_np_list
|
653 |
+
):
|
654 |
+
raise TypeError(
|
655 |
+
f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
|
656 |
+
)
|
657 |
+
|
658 |
+
if image_is_pil:
|
659 |
+
image_batch_size = 1
|
660 |
+
else:
|
661 |
+
image_batch_size = len(image)
|
662 |
+
|
663 |
+
if prompt is not None and isinstance(prompt, str):
|
664 |
+
prompt_batch_size = 1
|
665 |
+
elif prompt is not None and isinstance(prompt, list):
|
666 |
+
prompt_batch_size = len(prompt)
|
667 |
+
elif prompt_embeds is not None:
|
668 |
+
prompt_batch_size = prompt_embeds.shape[0]
|
669 |
+
|
670 |
+
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
|
671 |
+
raise ValueError(
|
672 |
+
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
|
673 |
+
)
|
674 |
+
|
675 |
+
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
|
676 |
+
def prepare_images(
|
677 |
+
self,
|
678 |
+
images,
|
679 |
+
width,
|
680 |
+
height,
|
681 |
+
batch_size,
|
682 |
+
num_images_per_prompt,
|
683 |
+
device,
|
684 |
+
dtype,
|
685 |
+
do_classifier_free_guidance=False,
|
686 |
+
guess_mode=False,
|
687 |
+
):
|
688 |
+
images_pre_processed = [self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) for image in images]
|
689 |
+
|
690 |
+
images_pre_processed = torch.cat(images_pre_processed, dim=0)
|
691 |
+
|
692 |
+
repeat_factor = [1] * len(images_pre_processed.shape)
|
693 |
+
repeat_factor[0] = batch_size * num_images_per_prompt
|
694 |
+
images_pre_processed = images_pre_processed.repeat(*repeat_factor)
|
695 |
+
|
696 |
+
images = images_pre_processed.unsqueeze(0)
|
697 |
+
|
698 |
+
# image_batch_size = image.shape[0]
|
699 |
+
#
|
700 |
+
# if image_batch_size == 1:
|
701 |
+
# repeat_by = batch_size
|
702 |
+
# else:
|
703 |
+
# # image batch size is the same as prompt batch size
|
704 |
+
# repeat_by = num_images_per_prompt
|
705 |
+
|
706 |
+
#image = image.repeat_interleave(repeat_by, dim=0)
|
707 |
+
|
708 |
+
images = images.to(device=device, dtype=dtype)
|
709 |
+
|
710 |
+
if do_classifier_free_guidance and not guess_mode:
|
711 |
+
repeat_factor = [1] * len(images.shape)
|
712 |
+
repeat_factor[0] = 2
|
713 |
+
images = images.repeat(*repeat_factor)
|
714 |
+
|
715 |
+
return images
|
716 |
+
|
717 |
+
# def prepare_images(self,
|
718 |
+
# images: list,
|
719 |
+
# width,
|
720 |
+
# height,
|
721 |
+
# batch_size,
|
722 |
+
# num_images_per_prompt,
|
723 |
+
# device,
|
724 |
+
# dtype,
|
725 |
+
# do_classifier_free_guidance=False,
|
726 |
+
# guess_mode=False):
|
727 |
+
#
|
728 |
+
# images = [self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) for image in images]
|
729 |
+
#
|
730 |
+
# image_batch_size = image.shape[0]
|
731 |
+
#
|
732 |
+
# if image_batch_size == 1:
|
733 |
+
# repeat_by = batch_size
|
734 |
+
# else:
|
735 |
+
# # image batch size is the same as prompt batch size
|
736 |
+
# repeat_by = num_images_per_prompt
|
737 |
+
#
|
738 |
+
# image = image.repeat_interleave(repeat_by, dim=0)
|
739 |
+
#
|
740 |
+
# image = image.to(device=device, dtype=dtype)
|
741 |
+
#
|
742 |
+
# if do_classifier_free_guidance and not guess_mode:
|
743 |
+
# image = torch.cat([image] * 2)
|
744 |
+
#
|
745 |
+
# return image
|
746 |
+
|
747 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
748 |
+
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
|
749 |
+
#shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
750 |
+
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
751 |
+
if isinstance(generator, list) and len(generator) != batch_size:
|
752 |
+
raise ValueError(
|
753 |
+
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
754 |
+
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
755 |
+
)
|
756 |
+
|
757 |
+
if latents is None:
|
758 |
+
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
759 |
+
else:
|
760 |
+
latents = latents.to(device)
|
761 |
+
|
762 |
+
# scale the initial noise by the standard deviation required by the scheduler
|
763 |
+
latents = latents * self.scheduler.init_noise_sigma
|
764 |
+
return latents
|
765 |
+
|
766 |
+
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._get_add_time_ids
|
767 |
+
def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
|
768 |
+
add_time_ids = list(original_size + crops_coords_top_left + target_size)
|
769 |
+
|
770 |
+
passed_add_embed_dim = (
|
771 |
+
self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
|
772 |
+
)
|
773 |
+
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
|
774 |
+
|
775 |
+
if expected_add_embed_dim != passed_add_embed_dim:
|
776 |
+
raise ValueError(
|
777 |
+
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`."
|
778 |
+
)
|
779 |
+
|
780 |
+
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
|
781 |
+
return add_time_ids
|
782 |
+
|
783 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
|
784 |
+
def upcast_vae(self):
|
785 |
+
dtype = self.vae.dtype
|
786 |
+
self.vae.to(dtype=torch.float32)
|
787 |
+
use_torch_2_0_or_xformers = isinstance(
|
788 |
+
self.vae.decoder.mid_block.attentions[0].processor,
|
789 |
+
(
|
790 |
+
AttnProcessor2_0,
|
791 |
+
XFormersAttnProcessor,
|
792 |
+
LoRAXFormersAttnProcessor,
|
793 |
+
LoRAAttnProcessor2_0,
|
794 |
+
),
|
795 |
+
)
|
796 |
+
# if xformers or torch_2_0 is used attention block does not need
|
797 |
+
# to be in float32 which can save lots of memory
|
798 |
+
if use_torch_2_0_or_xformers:
|
799 |
+
self.vae.post_quant_conv.to(dtype)
|
800 |
+
self.vae.decoder.conv_in.to(dtype)
|
801 |
+
self.vae.decoder.mid_block.to(dtype)
|
802 |
+
|
803 |
+
@torch.no_grad()
|
804 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
805 |
+
def __call__(
|
806 |
+
self,
|
807 |
+
prompt: Union[str, List[str]] = None,
|
808 |
+
prompt_2: Optional[Union[str, List[str]]] = None,
|
809 |
+
video_length: Optional[int] = 8,
|
810 |
+
control_images: List[PIL.Image.Image] = None,
|
811 |
+
height: Optional[int] = None,
|
812 |
+
width: Optional[int] = None,
|
813 |
+
num_inference_steps: int = 50,
|
814 |
+
guidance_scale: float = 5.0,
|
815 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
816 |
+
negative_prompt_2: Optional[Union[str, List[str]]] = None,
|
817 |
+
num_images_per_prompt: Optional[int] = 1,
|
818 |
+
eta: float = 0.0,
|
819 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
820 |
+
latents: Optional[torch.FloatTensor] = None,
|
821 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
822 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
823 |
+
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
824 |
+
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
825 |
+
output_type: Optional[str] = "pil",
|
826 |
+
return_dict: bool = True,
|
827 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
828 |
+
callback_steps: int = 1,
|
829 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
830 |
+
guidance_rescale: float = 0.0,
|
831 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
832 |
+
guess_mode: bool = False,
|
833 |
+
control_guidance_start: Union[float, List[float]] = 0.0,
|
834 |
+
control_guidance_end: Union[float, List[float]] = 1.0,
|
835 |
+
original_size: Tuple[int, int] = None,
|
836 |
+
crops_coords_top_left: Tuple[int, int] = (0, 0),
|
837 |
+
target_size: Tuple[int, int] = None,
|
838 |
+
negative_original_size: Optional[Tuple[int, int]] = None,
|
839 |
+
negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
|
840 |
+
negative_target_size: Optional[Tuple[int, int]] = None,
|
841 |
+
):
|
842 |
+
r"""
|
843 |
+
The call function to the pipeline for generation.
|
844 |
+
|
845 |
+
Args:
|
846 |
+
prompt (`str` or `List[str]`, *optional*):
|
847 |
+
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
848 |
+
prompt_2 (`str` or `List[str]`, *optional*):
|
849 |
+
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
850 |
+
used in both text-encoders.
|
851 |
+
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
|
852 |
+
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
|
853 |
+
The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
|
854 |
+
specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
|
855 |
+
accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
|
856 |
+
and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
|
857 |
+
`init`, images must be passed as a list such that each element of the list can be correctly batched for
|
858 |
+
input to a single ControlNet.
|
859 |
+
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
860 |
+
The height in pixels of the generated image.
|
861 |
+
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
862 |
+
The width in pixels of the generated image.
|
863 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
864 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
865 |
+
expense of slower inference.
|
866 |
+
guidance_scale (`float`, *optional*, defaults to 5.0):
|
867 |
+
A higher guidance scale value encourages the model to generate images closely linked to the text
|
868 |
+
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
869 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
870 |
+
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
871 |
+
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
872 |
+
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
873 |
+
The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
|
874 |
+
and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
|
875 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
876 |
+
The number of images to generate per prompt.
|
877 |
+
eta (`float`, *optional*, defaults to 0.0):
|
878 |
+
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
879 |
+
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
880 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
881 |
+
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
882 |
+
generation deterministic.
|
883 |
+
latents (`torch.FloatTensor`, *optional*):
|
884 |
+
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
885 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
886 |
+
tensor is generated by sampling using the supplied random `generator`.
|
887 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
888 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
889 |
+
provided, text embeddings are generated from the `prompt` input argument.
|
890 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
891 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
892 |
+
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
893 |
+
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
894 |
+
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
895 |
+
not provided, pooled text embeddings are generated from `prompt` input argument.
|
896 |
+
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
897 |
+
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
|
898 |
+
weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
|
899 |
+
argument.
|
900 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
901 |
+
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
902 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
903 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
904 |
+
plain tuple.
|
905 |
+
callback (`Callable`, *optional*):
|
906 |
+
A function that calls every `callback_steps` steps during inference. The function is called with the
|
907 |
+
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
908 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
909 |
+
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
910 |
+
every step.
|
911 |
+
cross_attention_kwargs (`dict`, *optional*):
|
912 |
+
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
913 |
+
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
914 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
915 |
+
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
|
916 |
+
to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
|
917 |
+
the corresponding scale as a list.
|
918 |
+
guess_mode (`bool`, *optional*, defaults to `False`):
|
919 |
+
The ControlNet encoder tries to recognize the content of the input image even if you remove all
|
920 |
+
prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
|
921 |
+
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
|
922 |
+
The percentage of total steps at which the ControlNet starts applying.
|
923 |
+
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
|
924 |
+
The percentage of total steps at which the ControlNet stops applying.
|
925 |
+
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
926 |
+
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
|
927 |
+
`original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
|
928 |
+
explained in section 2.2 of
|
929 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
930 |
+
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
931 |
+
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
|
932 |
+
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
|
933 |
+
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
|
934 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
935 |
+
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
936 |
+
For most cases, `target_size` should be set to the desired height and width of the generated image. If
|
937 |
+
not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
|
938 |
+
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
939 |
+
negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
940 |
+
To negatively condition the generation process based on a specific image resolution. Part of SDXL's
|
941 |
+
micro-conditioning as explained in section 2.2 of
|
942 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
943 |
+
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
944 |
+
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
945 |
+
To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
|
946 |
+
micro-conditioning as explained in section 2.2 of
|
947 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
948 |
+
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
949 |
+
negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
950 |
+
To negatively condition the generation process based on a target image resolution. It should be as same
|
951 |
+
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
|
952 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
|
953 |
+
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
|
954 |
+
|
955 |
+
Examples:
|
956 |
+
|
957 |
+
Returns:
|
958 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
959 |
+
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
960 |
+
otherwise a `tuple` is returned containing the output images.
|
961 |
+
"""
|
962 |
+
|
963 |
+
|
964 |
+
if video_length > 1 and num_images_per_prompt > 1:
|
965 |
+
print(f"Warning - setting num_images_per_prompt = 1 because video_length = {video_length}")
|
966 |
+
num_images_per_prompt = 1
|
967 |
+
|
968 |
+
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
|
969 |
+
|
970 |
+
# align format for control guidance
|
971 |
+
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
|
972 |
+
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
|
973 |
+
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
|
974 |
+
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
|
975 |
+
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
|
976 |
+
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
|
977 |
+
control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
|
978 |
+
control_guidance_end
|
979 |
+
]
|
980 |
+
|
981 |
+
# 1. Check inputs. Raise error if not correct
|
982 |
+
self.check_inputs(
|
983 |
+
prompt,
|
984 |
+
prompt_2,
|
985 |
+
control_images,
|
986 |
+
video_length,
|
987 |
+
callback_steps,
|
988 |
+
negative_prompt,
|
989 |
+
negative_prompt_2,
|
990 |
+
prompt_embeds,
|
991 |
+
negative_prompt_embeds,
|
992 |
+
pooled_prompt_embeds,
|
993 |
+
negative_pooled_prompt_embeds,
|
994 |
+
controlnet_conditioning_scale,
|
995 |
+
control_guidance_start,
|
996 |
+
control_guidance_end,
|
997 |
+
)
|
998 |
+
|
999 |
+
# 2. Define call parameters
|
1000 |
+
if prompt is not None and isinstance(prompt, str):
|
1001 |
+
batch_size = 1
|
1002 |
+
elif prompt is not None and isinstance(prompt, list):
|
1003 |
+
batch_size = len(prompt)
|
1004 |
+
else:
|
1005 |
+
batch_size = prompt_embeds.shape[0]
|
1006 |
+
|
1007 |
+
device = self._execution_device
|
1008 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
1009 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
1010 |
+
# corresponds to doing no classifier free guidance.
|
1011 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
1012 |
+
|
1013 |
+
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
|
1014 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
|
1015 |
+
|
1016 |
+
global_pool_conditions = (
|
1017 |
+
controlnet.config.global_pool_conditions
|
1018 |
+
if isinstance(controlnet, ControlNetModel)
|
1019 |
+
else controlnet.nets[0].config.global_pool_conditions
|
1020 |
+
)
|
1021 |
+
guess_mode = guess_mode or global_pool_conditions
|
1022 |
+
|
1023 |
+
# 3. Encode input prompt
|
1024 |
+
text_encoder_lora_scale = (
|
1025 |
+
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
1026 |
+
)
|
1027 |
+
(
|
1028 |
+
prompt_embeds,
|
1029 |
+
negative_prompt_embeds,
|
1030 |
+
pooled_prompt_embeds,
|
1031 |
+
negative_pooled_prompt_embeds,
|
1032 |
+
) = self.encode_prompt(
|
1033 |
+
prompt,
|
1034 |
+
prompt_2,
|
1035 |
+
device,
|
1036 |
+
num_images_per_prompt,
|
1037 |
+
do_classifier_free_guidance,
|
1038 |
+
negative_prompt,
|
1039 |
+
negative_prompt_2,
|
1040 |
+
prompt_embeds=prompt_embeds,
|
1041 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
1042 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
1043 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
1044 |
+
lora_scale=text_encoder_lora_scale,
|
1045 |
+
)
|
1046 |
+
|
1047 |
+
|
1048 |
+
# 4. Prepare image
|
1049 |
+
if isinstance(controlnet, ControlNetModel):
|
1050 |
+
|
1051 |
+
assert len(control_images) == video_length * batch_size
|
1052 |
+
|
1053 |
+
images = self.prepare_images(
|
1054 |
+
images=control_images,
|
1055 |
+
width=width,
|
1056 |
+
height=height,
|
1057 |
+
batch_size=batch_size * num_images_per_prompt,
|
1058 |
+
num_images_per_prompt=num_images_per_prompt,
|
1059 |
+
device=device,
|
1060 |
+
dtype=controlnet.dtype,
|
1061 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
1062 |
+
guess_mode=guess_mode,
|
1063 |
+
)
|
1064 |
+
|
1065 |
+
height, width = images.shape[-2:]
|
1066 |
+
elif isinstance(controlnet, MultiControlNetModel):
|
1067 |
+
|
1068 |
+
raise Exception("not supported yet")
|
1069 |
+
|
1070 |
+
# images = []
|
1071 |
+
#
|
1072 |
+
# for image_ in control_images:
|
1073 |
+
# image_ = self.prepare_image(
|
1074 |
+
# image=image_,
|
1075 |
+
# width=width,
|
1076 |
+
# height=height,
|
1077 |
+
# batch_size=batch_size * num_images_per_prompt,
|
1078 |
+
# num_images_per_prompt=num_images_per_prompt,
|
1079 |
+
# device=device,
|
1080 |
+
# dtype=controlnet.dtype,
|
1081 |
+
# do_classifier_free_guidance=do_classifier_free_guidance,
|
1082 |
+
# guess_mode=guess_mode,
|
1083 |
+
# )
|
1084 |
+
#
|
1085 |
+
# images.append(image_)
|
1086 |
+
#
|
1087 |
+
# image = images
|
1088 |
+
# height, width = image[0].shape[-2:]
|
1089 |
+
else:
|
1090 |
+
assert False
|
1091 |
+
|
1092 |
+
# 5. Prepare timesteps
|
1093 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
1094 |
+
timesteps = self.scheduler.timesteps
|
1095 |
+
|
1096 |
+
# 6. Prepare latent variables
|
1097 |
+
num_channels_latents = self.unet.config.in_channels
|
1098 |
+
latents = self.prepare_latents(
|
1099 |
+
batch_size * num_images_per_prompt,
|
1100 |
+
num_channels_latents,
|
1101 |
+
video_length,
|
1102 |
+
height,
|
1103 |
+
width,
|
1104 |
+
prompt_embeds.dtype,
|
1105 |
+
device,
|
1106 |
+
generator,
|
1107 |
+
latents,
|
1108 |
+
)
|
1109 |
+
|
1110 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
1111 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
1112 |
+
|
1113 |
+
# 7.1 Create tensor stating which controlnets to keep
|
1114 |
+
controlnet_keep = []
|
1115 |
+
for i in range(len(timesteps)):
|
1116 |
+
keeps = [
|
1117 |
+
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
|
1118 |
+
for s, e in zip(control_guidance_start, control_guidance_end)
|
1119 |
+
]
|
1120 |
+
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
|
1121 |
+
|
1122 |
+
# 7.2 Prepare added time ids & embeddings
|
1123 |
+
# if isinstance(image, list):
|
1124 |
+
# original_size = original_size or image[0].shape[-2:]
|
1125 |
+
# else:
|
1126 |
+
original_size = original_size or images.shape[-2:]
|
1127 |
+
target_size = target_size or (height, width)
|
1128 |
+
|
1129 |
+
add_text_embeds = pooled_prompt_embeds
|
1130 |
+
add_time_ids = self._get_add_time_ids(
|
1131 |
+
original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
|
1132 |
+
)
|
1133 |
+
|
1134 |
+
if negative_original_size is not None and negative_target_size is not None:
|
1135 |
+
negative_add_time_ids = self._get_add_time_ids(
|
1136 |
+
negative_original_size,
|
1137 |
+
negative_crops_coords_top_left,
|
1138 |
+
negative_target_size,
|
1139 |
+
dtype=prompt_embeds.dtype,
|
1140 |
+
)
|
1141 |
+
else:
|
1142 |
+
negative_add_time_ids = add_time_ids
|
1143 |
+
|
1144 |
+
if do_classifier_free_guidance:
|
1145 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
1146 |
+
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
|
1147 |
+
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
|
1148 |
+
|
1149 |
+
prompt_embeds = prompt_embeds.to(device)
|
1150 |
+
add_text_embeds = add_text_embeds.to(device)
|
1151 |
+
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
|
1152 |
+
|
1153 |
+
# 8. Denoising loop
|
1154 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
1155 |
+
|
1156 |
+
images = rearrange(images, "b f c h w -> (b f) c h w")
|
1157 |
+
|
1158 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
1159 |
+
for i, t in enumerate(timesteps):
|
1160 |
+
# expand the latents if we are doing classifier free guidance
|
1161 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
1162 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
1163 |
+
|
1164 |
+
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
1165 |
+
|
1166 |
+
# controlnet(s) inference
|
1167 |
+
if guess_mode and do_classifier_free_guidance:
|
1168 |
+
# Infer ControlNet only for the conditional batch.
|
1169 |
+
control_model_input = latents
|
1170 |
+
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
|
1171 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
1172 |
+
controlnet_added_cond_kwargs = {
|
1173 |
+
"text_embeds": add_text_embeds.chunk(2)[1],
|
1174 |
+
"time_ids": add_time_ids.chunk(2)[1],
|
1175 |
+
}
|
1176 |
+
else:
|
1177 |
+
control_model_input = latent_model_input
|
1178 |
+
controlnet_prompt_embeds = prompt_embeds
|
1179 |
+
controlnet_added_cond_kwargs = added_cond_kwargs
|
1180 |
+
|
1181 |
+
if isinstance(controlnet_keep[i], list):
|
1182 |
+
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
|
1183 |
+
else:
|
1184 |
+
controlnet_cond_scale = controlnet_conditioning_scale
|
1185 |
+
if isinstance(controlnet_cond_scale, list):
|
1186 |
+
controlnet_cond_scale = controlnet_cond_scale[0]
|
1187 |
+
cond_scale = controlnet_cond_scale * controlnet_keep[i]
|
1188 |
+
|
1189 |
+
|
1190 |
+
# this will be non interlaced when arranged!
|
1191 |
+
control_model_input = rearrange(control_model_input, "b c f h w -> (b f) c h w")
|
1192 |
+
# if we chunked this by 2 - the top 8 frames will be positive for cfg
|
1193 |
+
# the bottom half will be negative for cfg...
|
1194 |
+
|
1195 |
+
if video_length > 1:
|
1196 |
+
# use repeat_interleave as we need to match the rearrangement above.
|
1197 |
+
|
1198 |
+
controlnet_prompt_embeds = controlnet_prompt_embeds.repeat_interleave(video_length, dim=0)
|
1199 |
+
controlnet_added_cond_kwargs = {
|
1200 |
+
"text_embeds": controlnet_added_cond_kwargs['text_embeds'].repeat_interleave(video_length, dim=0),
|
1201 |
+
"time_ids": controlnet_added_cond_kwargs['time_ids'].repeat_interleave(video_length, dim=0)
|
1202 |
+
}
|
1203 |
+
|
1204 |
+
# if type(image) is list:
|
1205 |
+
# image = torch.cat(image, dim=0)
|
1206 |
+
|
1207 |
+
# todo - check if video_length > 1 this needs to produce num_frames * batch_size samples...
|
1208 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
1209 |
+
control_model_input,
|
1210 |
+
t,
|
1211 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
1212 |
+
controlnet_cond=images,
|
1213 |
+
conditioning_scale=cond_scale,
|
1214 |
+
guess_mode=guess_mode,
|
1215 |
+
added_cond_kwargs=controlnet_added_cond_kwargs,
|
1216 |
+
return_dict=False,
|
1217 |
+
)
|
1218 |
+
|
1219 |
+
for j, sample in enumerate(down_block_res_samples):
|
1220 |
+
down_block_res_samples[j] = rearrange(sample, "(b f) c h w -> b c f h w", f=video_length)
|
1221 |
+
|
1222 |
+
mid_block_res_sample = rearrange(mid_block_res_sample, "(b f) c h w -> b c f h w", f=video_length)
|
1223 |
+
|
1224 |
+
if guess_mode and do_classifier_free_guidance:
|
1225 |
+
# Infered ControlNet only for the conditional batch.
|
1226 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
1227 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
1228 |
+
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
|
1229 |
+
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
1230 |
+
|
1231 |
+
# predict the noise residual
|
1232 |
+
noise_pred = self.unet(
|
1233 |
+
latent_model_input,
|
1234 |
+
t,
|
1235 |
+
encoder_hidden_states=prompt_embeds,
|
1236 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
1237 |
+
down_block_additional_residuals=down_block_res_samples,
|
1238 |
+
mid_block_additional_residual=mid_block_res_sample,
|
1239 |
+
added_cond_kwargs=added_cond_kwargs,
|
1240 |
+
return_dict=False,
|
1241 |
+
enable_temporal_attentions=video_length > 1
|
1242 |
+
)[0]
|
1243 |
+
|
1244 |
+
# perform guidance
|
1245 |
+
if do_classifier_free_guidance:
|
1246 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
1247 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
1248 |
+
|
1249 |
+
if do_classifier_free_guidance and guidance_rescale > 0.0:
|
1250 |
+
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
1251 |
+
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
1252 |
+
|
1253 |
+
# compute the previous noisy sample x_t -> x_t-1
|
1254 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
1255 |
+
|
1256 |
+
# call the callback, if provided
|
1257 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
1258 |
+
progress_bar.update()
|
1259 |
+
if callback is not None and i % callback_steps == 0:
|
1260 |
+
callback(i, t, latents)
|
1261 |
+
|
1262 |
+
# make sure the VAE is in float32 mode, as it overflows in float16
|
1263 |
+
if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
|
1264 |
+
self.upcast_vae()
|
1265 |
+
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
1266 |
+
|
1267 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
1268 |
+
# manually for max memory savings
|
1269 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
1270 |
+
self.unet.to("cpu")
|
1271 |
+
self.controlnet.to("cpu")
|
1272 |
+
torch.cuda.empty_cache()
|
1273 |
+
|
1274 |
+
# if not output_type == "latent":
|
1275 |
+
# # make sure the VAE is in float32 mode, as it overflows in float16
|
1276 |
+
# needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
|
1277 |
+
#
|
1278 |
+
# if needs_upcasting:
|
1279 |
+
# self.upcast_vae()
|
1280 |
+
# latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
1281 |
+
#
|
1282 |
+
# image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
1283 |
+
#
|
1284 |
+
# # cast back to fp16 if needed
|
1285 |
+
# if needs_upcasting:
|
1286 |
+
# self.vae.to(dtype=torch.float16)
|
1287 |
+
# else:
|
1288 |
+
# image = latents
|
1289 |
+
# return StableDiffusionXLPipelineOutput(images=image)
|
1290 |
+
|
1291 |
+
video = self.decode_latents(latents)
|
1292 |
+
|
1293 |
+
# Convert to tensor
|
1294 |
+
if output_type == "tensor":
|
1295 |
+
video = torch.from_numpy(video)
|
1296 |
+
|
1297 |
+
if not return_dict:
|
1298 |
+
return video
|
1299 |
+
|
1300 |
+
return HotshotPipelineXLOutput(videos=video)
|
1301 |
+
|
1302 |
+
def decode_latents(self, latents):
|
1303 |
+
video_length = latents.shape[2]
|
1304 |
+
latents = 1 / self.vae.config.scaling_factor * latents
|
1305 |
+
latents = rearrange(latents, "b c f h w -> (b f) c h w")
|
1306 |
+
# video = self.vae.decode(latents).sample
|
1307 |
+
video = []
|
1308 |
+
for frame_idx in tqdm(range(latents.shape[0])):
|
1309 |
+
video.append(self.vae.decode(
|
1310 |
+
latents[frame_idx:frame_idx+1]).sample)
|
1311 |
+
video = torch.cat(video)
|
1312 |
+
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
|
1313 |
+
video = (video / 2.0 + 0.5).clamp(0, 1)
|
1314 |
+
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
1315 |
+
video = video.cpu().float().numpy()
|
1316 |
+
return video
|
1317 |
+
|
1318 |
+
# Overrride to properly handle the loading and unloading of the additional text encoder.
|
1319 |
+
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.load_lora_weights
|
1320 |
+
def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
|
1321 |
+
# We could have accessed the unet config from `lora_state_dict()` too. We pass
|
1322 |
+
# it here explicitly to be able to tell that it's coming from an SDXL
|
1323 |
+
# pipeline.
|
1324 |
+
state_dict, network_alphas = self.lora_state_dict(
|
1325 |
+
pretrained_model_name_or_path_or_dict,
|
1326 |
+
unet_config=self.unet.config,
|
1327 |
+
**kwargs,
|
1328 |
+
)
|
1329 |
+
self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
|
1330 |
+
|
1331 |
+
text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
|
1332 |
+
if len(text_encoder_state_dict) > 0:
|
1333 |
+
self.load_lora_into_text_encoder(
|
1334 |
+
text_encoder_state_dict,
|
1335 |
+
network_alphas=network_alphas,
|
1336 |
+
text_encoder=self.text_encoder,
|
1337 |
+
prefix="text_encoder",
|
1338 |
+
lora_scale=self.lora_scale,
|
1339 |
+
)
|
1340 |
+
|
1341 |
+
text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
|
1342 |
+
if len(text_encoder_2_state_dict) > 0:
|
1343 |
+
self.load_lora_into_text_encoder(
|
1344 |
+
text_encoder_2_state_dict,
|
1345 |
+
network_alphas=network_alphas,
|
1346 |
+
text_encoder=self.text_encoder_2,
|
1347 |
+
prefix="text_encoder_2",
|
1348 |
+
lora_scale=self.lora_scale,
|
1349 |
+
)
|
1350 |
+
|
1351 |
+
@classmethod
|
1352 |
+
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.save_lora_weights
|
1353 |
+
def save_lora_weights(
|
1354 |
+
self,
|
1355 |
+
save_directory: Union[str, os.PathLike],
|
1356 |
+
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
|
1357 |
+
text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
|
1358 |
+
text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
|
1359 |
+
is_main_process: bool = True,
|
1360 |
+
weight_name: str = None,
|
1361 |
+
save_function: Callable = None,
|
1362 |
+
safe_serialization: bool = True,
|
1363 |
+
):
|
1364 |
+
state_dict = {}
|
1365 |
+
|
1366 |
+
def pack_weights(layers, prefix):
|
1367 |
+
layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
|
1368 |
+
layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
|
1369 |
+
return layers_state_dict
|
1370 |
+
|
1371 |
+
state_dict.update(pack_weights(unet_lora_layers, "unet"))
|
1372 |
+
|
1373 |
+
if text_encoder_lora_layers and text_encoder_2_lora_layers:
|
1374 |
+
state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
|
1375 |
+
state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
|
1376 |
+
|
1377 |
+
self.write_lora_layers(
|
1378 |
+
state_dict=state_dict,
|
1379 |
+
save_directory=save_directory,
|
1380 |
+
is_main_process=is_main_process,
|
1381 |
+
weight_name=weight_name,
|
1382 |
+
save_function=save_function,
|
1383 |
+
safe_serialization=safe_serialization,
|
1384 |
+
)
|
1385 |
+
|
1386 |
+
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline._remove_text_encoder_monkey_patch
|
1387 |
+
def _remove_text_encoder_monkey_patch(self):
|
1388 |
+
self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
|
1389 |
+
self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
|
hotshot_xl_pipeline.py
ADDED
@@ -0,0 +1,996 @@
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|
|
1 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
# Modifications:
|
16 |
+
# Copyright 2023 Natural Synthetics Inc. All rights reserved.
|
17 |
+
# - Adapted the SDXL Pipeline to work temporally
|
18 |
+
|
19 |
+
|
20 |
+
import os
|
21 |
+
import inspect
|
22 |
+
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
|
23 |
+
|
24 |
+
import torch
|
25 |
+
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
|
26 |
+
from hotshot_xl import HotshotPipelineXLOutput
|
27 |
+
|
28 |
+
from diffusers.image_processor import VaeImageProcessor
|
29 |
+
from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
|
30 |
+
from diffusers.models import AutoencoderKL
|
31 |
+
from hotshot_xl.models.unet import UNet3DConditionModel
|
32 |
+
from diffusers.models.attention_processor import (
|
33 |
+
AttnProcessor2_0,
|
34 |
+
LoRAAttnProcessor2_0,
|
35 |
+
LoRAXFormersAttnProcessor,
|
36 |
+
XFormersAttnProcessor,
|
37 |
+
)
|
38 |
+
from diffusers.schedulers import KarrasDiffusionSchedulers
|
39 |
+
from diffusers.utils import (
|
40 |
+
is_accelerate_available,
|
41 |
+
is_accelerate_version,
|
42 |
+
logging,
|
43 |
+
replace_example_docstring,
|
44 |
+
)
|
45 |
+
from diffusers.utils.torch_utils import randn_tensor
|
46 |
+
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
47 |
+
from tqdm import tqdm
|
48 |
+
from einops import repeat, rearrange
|
49 |
+
from diffusers.utils import deprecate, logging
|
50 |
+
import gc
|
51 |
+
|
52 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
53 |
+
|
54 |
+
EXAMPLE_DOC_STRING = """
|
55 |
+
Examples:
|
56 |
+
```py
|
57 |
+
>>> import torch
|
58 |
+
>>> from hotshot_xl import HotshotPipelineXL
|
59 |
+
|
60 |
+
>>> pipe = HotshotXLPipeline.from_pretrained(
|
61 |
+
... "hotshotco/Hotshot-XL"
|
62 |
+
... )
|
63 |
+
>>> pipe = pipe.to("cuda")
|
64 |
+
|
65 |
+
>>> prompt = "a photo of an astronaut riding a horse on mars"
|
66 |
+
>>> video = pipe(prompt,
|
67 |
+
... width=672, height=384,
|
68 |
+
... original_size=(1920, 1080),
|
69 |
+
... target_size=(512, 512),
|
70 |
+
... output_type="tensor"
|
71 |
+
).video
|
72 |
+
```
|
73 |
+
"""
|
74 |
+
|
75 |
+
|
76 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
|
77 |
+
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
|
78 |
+
"""
|
79 |
+
Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
|
80 |
+
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
|
81 |
+
"""
|
82 |
+
std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
|
83 |
+
std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
|
84 |
+
# rescale the results from guidance (fixes overexposure)
|
85 |
+
noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
|
86 |
+
# mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
|
87 |
+
noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
|
88 |
+
return noise_cfg
|
89 |
+
|
90 |
+
|
91 |
+
|
92 |
+
|
93 |
+
class HotshotXLPipeline(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
|
94 |
+
r"""
|
95 |
+
Pipeline for text-to-image generation using Stable Diffusion XL.
|
96 |
+
|
97 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
|
98 |
+
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
|
99 |
+
|
100 |
+
In addition the pipeline inherits the following loading methods:
|
101 |
+
- *LoRA*: [`HotshotPipelineXL.load_lora_weights`]
|
102 |
+
- *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
|
103 |
+
|
104 |
+
as well as the following saving methods:
|
105 |
+
- *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]
|
106 |
+
|
107 |
+
Args:
|
108 |
+
vae ([`AutoencoderKL`]):
|
109 |
+
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
|
110 |
+
text_encoder ([`CLIPTextModel`]):
|
111 |
+
Frozen text-encoder. Stable Diffusion XL uses the text portion of
|
112 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
|
113 |
+
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
|
114 |
+
text_encoder_2 ([` CLIPTextModelWithProjection`]):
|
115 |
+
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
|
116 |
+
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
|
117 |
+
specifically the
|
118 |
+
[laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
|
119 |
+
variant.
|
120 |
+
tokenizer (`CLIPTokenizer`):
|
121 |
+
Tokenizer of class
|
122 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
123 |
+
tokenizer_2 (`CLIPTokenizer`):
|
124 |
+
Second Tokenizer of class
|
125 |
+
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
|
126 |
+
unet ([`UNet3DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
|
127 |
+
scheduler ([`SchedulerMixin`]):
|
128 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
129 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
130 |
+
"""
|
131 |
+
|
132 |
+
def __init__(
|
133 |
+
self,
|
134 |
+
vae: AutoencoderKL,
|
135 |
+
text_encoder: CLIPTextModel,
|
136 |
+
text_encoder_2: CLIPTextModelWithProjection,
|
137 |
+
tokenizer: CLIPTokenizer,
|
138 |
+
tokenizer_2: CLIPTokenizer,
|
139 |
+
unet: UNet3DConditionModel,
|
140 |
+
scheduler: KarrasDiffusionSchedulers,
|
141 |
+
force_zeros_for_empty_prompt: bool = True,
|
142 |
+
add_watermarker: Optional[bool] = None,
|
143 |
+
):
|
144 |
+
super().__init__()
|
145 |
+
|
146 |
+
self.register_modules(
|
147 |
+
vae=vae,
|
148 |
+
text_encoder=text_encoder,
|
149 |
+
text_encoder_2=text_encoder_2,
|
150 |
+
tokenizer=tokenizer,
|
151 |
+
tokenizer_2=tokenizer_2,
|
152 |
+
unet=unet,
|
153 |
+
scheduler=scheduler,
|
154 |
+
)
|
155 |
+
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
|
156 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
157 |
+
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
158 |
+
self.default_sample_size = self.unet.config.sample_size
|
159 |
+
self.watermark = None
|
160 |
+
|
161 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
|
162 |
+
def enable_vae_slicing(self):
|
163 |
+
r"""
|
164 |
+
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
|
165 |
+
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
|
166 |
+
"""
|
167 |
+
self.vae.enable_slicing()
|
168 |
+
|
169 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
|
170 |
+
def disable_vae_slicing(self):
|
171 |
+
r"""
|
172 |
+
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
|
173 |
+
computing decoding in one step.
|
174 |
+
"""
|
175 |
+
self.vae.disable_slicing()
|
176 |
+
|
177 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
|
178 |
+
def enable_vae_tiling(self):
|
179 |
+
r"""
|
180 |
+
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
|
181 |
+
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
|
182 |
+
processing larger images.
|
183 |
+
"""
|
184 |
+
self.vae.enable_tiling()
|
185 |
+
|
186 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
|
187 |
+
def disable_vae_tiling(self):
|
188 |
+
r"""
|
189 |
+
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
|
190 |
+
computing decoding in one step.
|
191 |
+
"""
|
192 |
+
self.vae.disable_tiling()
|
193 |
+
|
194 |
+
def enable_model_cpu_offload(self, gpu_id=0):
|
195 |
+
r"""
|
196 |
+
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
|
197 |
+
to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
|
198 |
+
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
|
199 |
+
`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
|
200 |
+
"""
|
201 |
+
if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
|
202 |
+
from accelerate import cpu_offload_with_hook
|
203 |
+
else:
|
204 |
+
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
|
205 |
+
|
206 |
+
device = torch.device(f"cuda:{gpu_id}")
|
207 |
+
|
208 |
+
if self.device.type != "cpu":
|
209 |
+
self.to("cpu", silence_dtype_warnings=True)
|
210 |
+
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
|
211 |
+
|
212 |
+
model_sequence = (
|
213 |
+
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
|
214 |
+
)
|
215 |
+
model_sequence.extend([self.unet, self.vae])
|
216 |
+
|
217 |
+
hook = None
|
218 |
+
for cpu_offloaded_model in model_sequence:
|
219 |
+
_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
|
220 |
+
|
221 |
+
# We'll offload the last model manually.
|
222 |
+
self.final_offload_hook = hook
|
223 |
+
|
224 |
+
def encode_prompt(
|
225 |
+
self,
|
226 |
+
prompt: str,
|
227 |
+
prompt_2: Optional[str] = None,
|
228 |
+
device: Optional[torch.device] = None,
|
229 |
+
num_images_per_prompt: int = 1,
|
230 |
+
do_classifier_free_guidance: bool = True,
|
231 |
+
negative_prompt: Optional[str] = None,
|
232 |
+
negative_prompt_2: Optional[str] = None,
|
233 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
234 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
235 |
+
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
236 |
+
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
237 |
+
lora_scale: Optional[float] = None,
|
238 |
+
):
|
239 |
+
r"""
|
240 |
+
Encodes the prompt into text encoder hidden states.
|
241 |
+
|
242 |
+
Args:
|
243 |
+
prompt (`str` or `List[str]`, *optional*):
|
244 |
+
prompt to be encoded
|
245 |
+
prompt_2 (`str` or `List[str]`, *optional*):
|
246 |
+
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
247 |
+
used in both text-encoders
|
248 |
+
device: (`torch.device`):
|
249 |
+
torch device
|
250 |
+
num_images_per_prompt (`int`):
|
251 |
+
number of images that should be generated per prompt
|
252 |
+
do_classifier_free_guidance (`bool`):
|
253 |
+
whether to use classifier free guidance or not
|
254 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
255 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
256 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
257 |
+
less than `1`).
|
258 |
+
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
259 |
+
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
260 |
+
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
|
261 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
262 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
263 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
264 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
265 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
266 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
267 |
+
argument.
|
268 |
+
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
269 |
+
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
270 |
+
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
271 |
+
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
272 |
+
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
273 |
+
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
274 |
+
input argument.
|
275 |
+
lora_scale (`float`, *optional*):
|
276 |
+
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
|
277 |
+
"""
|
278 |
+
device = device or self._execution_device
|
279 |
+
|
280 |
+
# set lora scale so that monkey patched LoRA
|
281 |
+
# function of text encoder can correctly access it
|
282 |
+
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
|
283 |
+
self._lora_scale = lora_scale
|
284 |
+
|
285 |
+
if prompt is not None and isinstance(prompt, str):
|
286 |
+
batch_size = 1
|
287 |
+
elif prompt is not None and isinstance(prompt, list):
|
288 |
+
batch_size = len(prompt)
|
289 |
+
else:
|
290 |
+
batch_size = prompt_embeds.shape[0]
|
291 |
+
|
292 |
+
# Define tokenizers and text encoders
|
293 |
+
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
|
294 |
+
text_encoders = (
|
295 |
+
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
|
296 |
+
)
|
297 |
+
|
298 |
+
if prompt_embeds is None:
|
299 |
+
prompt_2 = prompt_2 or prompt
|
300 |
+
# textual inversion: procecss multi-vector tokens if necessary
|
301 |
+
prompt_embeds_list = []
|
302 |
+
prompts = [prompt, prompt_2]
|
303 |
+
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
|
304 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
305 |
+
prompt = self.maybe_convert_prompt(prompt, tokenizer)
|
306 |
+
|
307 |
+
text_inputs = tokenizer(
|
308 |
+
prompt,
|
309 |
+
padding="max_length",
|
310 |
+
max_length=tokenizer.model_max_length,
|
311 |
+
truncation=True,
|
312 |
+
return_tensors="pt",
|
313 |
+
)
|
314 |
+
|
315 |
+
text_input_ids = text_inputs.input_ids
|
316 |
+
untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
317 |
+
|
318 |
+
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
319 |
+
text_input_ids, untruncated_ids
|
320 |
+
):
|
321 |
+
removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
|
322 |
+
logger.warning(
|
323 |
+
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
324 |
+
f" {tokenizer.model_max_length} tokens: {removed_text}"
|
325 |
+
)
|
326 |
+
|
327 |
+
prompt_embeds = text_encoder(
|
328 |
+
text_input_ids.to(device),
|
329 |
+
output_hidden_states=True,
|
330 |
+
)
|
331 |
+
|
332 |
+
# We are only ALWAYS interested in the pooled output of the final text encoder
|
333 |
+
pooled_prompt_embeds = prompt_embeds[0]
|
334 |
+
prompt_embeds = prompt_embeds.hidden_states[-2]
|
335 |
+
|
336 |
+
prompt_embeds_list.append(prompt_embeds)
|
337 |
+
|
338 |
+
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
|
339 |
+
|
340 |
+
# get unconditional embeddings for classifier free guidance
|
341 |
+
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
|
342 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
|
343 |
+
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
|
344 |
+
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
|
345 |
+
elif do_classifier_free_guidance and negative_prompt_embeds is None:
|
346 |
+
negative_prompt = negative_prompt or ""
|
347 |
+
negative_prompt_2 = negative_prompt_2 or negative_prompt
|
348 |
+
|
349 |
+
uncond_tokens: List[str]
|
350 |
+
if prompt is not None and type(prompt) is not type(negative_prompt):
|
351 |
+
raise TypeError(
|
352 |
+
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
353 |
+
f" {type(prompt)}."
|
354 |
+
)
|
355 |
+
elif isinstance(negative_prompt, str):
|
356 |
+
uncond_tokens = [negative_prompt, negative_prompt_2]
|
357 |
+
elif batch_size != len(negative_prompt):
|
358 |
+
raise ValueError(
|
359 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
360 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
361 |
+
" the batch size of `prompt`."
|
362 |
+
)
|
363 |
+
else:
|
364 |
+
uncond_tokens = [negative_prompt, negative_prompt_2]
|
365 |
+
|
366 |
+
negative_prompt_embeds_list = []
|
367 |
+
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
|
368 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
369 |
+
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
|
370 |
+
|
371 |
+
max_length = prompt_embeds.shape[1]
|
372 |
+
uncond_input = tokenizer(
|
373 |
+
negative_prompt,
|
374 |
+
padding="max_length",
|
375 |
+
max_length=max_length,
|
376 |
+
truncation=True,
|
377 |
+
return_tensors="pt",
|
378 |
+
)
|
379 |
+
|
380 |
+
negative_prompt_embeds = text_encoder(
|
381 |
+
uncond_input.input_ids.to(device),
|
382 |
+
output_hidden_states=True,
|
383 |
+
)
|
384 |
+
# We are only ALWAYS interested in the pooled output of the final text encoder
|
385 |
+
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
|
386 |
+
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
|
387 |
+
|
388 |
+
negative_prompt_embeds_list.append(negative_prompt_embeds)
|
389 |
+
|
390 |
+
negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
|
391 |
+
|
392 |
+
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
393 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
394 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
395 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
396 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
397 |
+
|
398 |
+
if do_classifier_free_guidance:
|
399 |
+
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
400 |
+
seq_len = negative_prompt_embeds.shape[1]
|
401 |
+
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
402 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
403 |
+
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
404 |
+
|
405 |
+
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
406 |
+
bs_embed * num_images_per_prompt, -1
|
407 |
+
)
|
408 |
+
if do_classifier_free_guidance:
|
409 |
+
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
|
410 |
+
bs_embed * num_images_per_prompt, -1
|
411 |
+
)
|
412 |
+
|
413 |
+
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
|
414 |
+
|
415 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
416 |
+
def prepare_extra_step_kwargs(self, generator, eta):
|
417 |
+
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
418 |
+
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
419 |
+
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
420 |
+
# and should be between [0, 1]
|
421 |
+
|
422 |
+
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
423 |
+
extra_step_kwargs = {}
|
424 |
+
if accepts_eta:
|
425 |
+
extra_step_kwargs["eta"] = eta
|
426 |
+
|
427 |
+
# check if the scheduler accepts generator
|
428 |
+
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
429 |
+
if accepts_generator:
|
430 |
+
extra_step_kwargs["generator"] = generator
|
431 |
+
return extra_step_kwargs
|
432 |
+
|
433 |
+
def check_inputs(
|
434 |
+
self,
|
435 |
+
prompt,
|
436 |
+
prompt_2,
|
437 |
+
height,
|
438 |
+
width,
|
439 |
+
callback_steps,
|
440 |
+
negative_prompt=None,
|
441 |
+
negative_prompt_2=None,
|
442 |
+
prompt_embeds=None,
|
443 |
+
negative_prompt_embeds=None,
|
444 |
+
pooled_prompt_embeds=None,
|
445 |
+
negative_pooled_prompt_embeds=None,
|
446 |
+
):
|
447 |
+
if height % 8 != 0 or width % 8 != 0:
|
448 |
+
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
449 |
+
|
450 |
+
if (callback_steps is None) or (
|
451 |
+
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
|
452 |
+
):
|
453 |
+
raise ValueError(
|
454 |
+
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
455 |
+
f" {type(callback_steps)}."
|
456 |
+
)
|
457 |
+
|
458 |
+
if prompt is not None and prompt_embeds is not None:
|
459 |
+
raise ValueError(
|
460 |
+
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
461 |
+
" only forward one of the two."
|
462 |
+
)
|
463 |
+
elif prompt_2 is not None and prompt_embeds is not None:
|
464 |
+
raise ValueError(
|
465 |
+
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
466 |
+
" only forward one of the two."
|
467 |
+
)
|
468 |
+
elif prompt is None and prompt_embeds is None:
|
469 |
+
raise ValueError(
|
470 |
+
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
471 |
+
)
|
472 |
+
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
473 |
+
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
474 |
+
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
|
475 |
+
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
|
476 |
+
|
477 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
478 |
+
raise ValueError(
|
479 |
+
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
480 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
481 |
+
)
|
482 |
+
elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
|
483 |
+
raise ValueError(
|
484 |
+
f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
|
485 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
486 |
+
)
|
487 |
+
|
488 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
489 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
490 |
+
raise ValueError(
|
491 |
+
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
492 |
+
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
493 |
+
f" {negative_prompt_embeds.shape}."
|
494 |
+
)
|
495 |
+
|
496 |
+
if prompt_embeds is not None and pooled_prompt_embeds is None:
|
497 |
+
raise ValueError(
|
498 |
+
"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`."
|
499 |
+
)
|
500 |
+
|
501 |
+
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
|
502 |
+
raise ValueError(
|
503 |
+
"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`."
|
504 |
+
)
|
505 |
+
|
506 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
507 |
+
def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None):
|
508 |
+
shape = (batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
509 |
+
if isinstance(generator, list) and len(generator) != batch_size:
|
510 |
+
raise ValueError(
|
511 |
+
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
512 |
+
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
513 |
+
)
|
514 |
+
|
515 |
+
if latents is None:
|
516 |
+
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
517 |
+
else:
|
518 |
+
latents = latents.to(device)
|
519 |
+
|
520 |
+
# scale the initial noise by the standard deviation required by the scheduler
|
521 |
+
latents = latents * self.scheduler.init_noise_sigma
|
522 |
+
return latents
|
523 |
+
|
524 |
+
def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
|
525 |
+
add_time_ids = list(original_size + crops_coords_top_left + target_size)
|
526 |
+
|
527 |
+
passed_add_embed_dim = (
|
528 |
+
self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
|
529 |
+
)
|
530 |
+
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
|
531 |
+
|
532 |
+
if expected_add_embed_dim != passed_add_embed_dim:
|
533 |
+
raise ValueError(
|
534 |
+
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`."
|
535 |
+
)
|
536 |
+
|
537 |
+
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
|
538 |
+
return add_time_ids
|
539 |
+
|
540 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
|
541 |
+
def upcast_vae(self):
|
542 |
+
dtype = self.vae.dtype
|
543 |
+
self.vae.to(dtype=torch.float32)
|
544 |
+
use_torch_2_0_or_xformers = isinstance(
|
545 |
+
self.vae.decoder.mid_block.attentions[0].processor,
|
546 |
+
(
|
547 |
+
AttnProcessor2_0,
|
548 |
+
XFormersAttnProcessor,
|
549 |
+
LoRAXFormersAttnProcessor,
|
550 |
+
LoRAAttnProcessor2_0,
|
551 |
+
),
|
552 |
+
)
|
553 |
+
# if xformers or torch_2_0 is used attention block does not need
|
554 |
+
# to be in float32 which can save lots of memory
|
555 |
+
if use_torch_2_0_or_xformers:
|
556 |
+
self.vae.post_quant_conv.to(dtype)
|
557 |
+
self.vae.decoder.conv_in.to(dtype)
|
558 |
+
self.vae.decoder.mid_block.to(dtype)
|
559 |
+
|
560 |
+
@torch.no_grad()
|
561 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
562 |
+
def __call__(
|
563 |
+
self,
|
564 |
+
prompt: Union[str, List[str]] = None,
|
565 |
+
prompt_2: Optional[Union[str, List[str]]] = None,
|
566 |
+
video_length: Optional[int] = 8,
|
567 |
+
num_images_per_prompt: Optional[int] = 1,
|
568 |
+
height: Optional[int] = None,
|
569 |
+
width: Optional[int] = None,
|
570 |
+
num_inference_steps: int = 50,
|
571 |
+
denoising_end: Optional[float] = None,
|
572 |
+
guidance_scale: float = 5.0,
|
573 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
574 |
+
negative_prompt_2: Optional[Union[str, List[str]]] = None,
|
575 |
+
eta: float = 0.0,
|
576 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
577 |
+
latents: Optional[torch.FloatTensor] = None,
|
578 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
579 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
580 |
+
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
581 |
+
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
|
582 |
+
output_type: Optional[str] = "pil",
|
583 |
+
return_dict: bool = True,
|
584 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
585 |
+
callback_steps: int = 1,
|
586 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
587 |
+
guidance_rescale: float = 0.0,
|
588 |
+
original_size: Optional[Tuple[int, int]] = None,
|
589 |
+
crops_coords_top_left: Tuple[int, int] = (0, 0),
|
590 |
+
target_size: Optional[Tuple[int, int]] = None,
|
591 |
+
low_vram_mode: Optional[bool] = False
|
592 |
+
):
|
593 |
+
r"""
|
594 |
+
Function invoked when calling the pipeline for generation.
|
595 |
+
|
596 |
+
Args:
|
597 |
+
prompt (`str` or `List[str]`, *optional*):
|
598 |
+
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
|
599 |
+
instead.
|
600 |
+
prompt_2 (`str` or `List[str]`, *optional*):
|
601 |
+
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
602 |
+
used in both text-encoders
|
603 |
+
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
604 |
+
The height in pixels of the generated image.
|
605 |
+
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
|
606 |
+
The width in pixels of the generated image.
|
607 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
608 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
609 |
+
expense of slower inference.
|
610 |
+
denoising_end (`float`, *optional*):
|
611 |
+
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
|
612 |
+
completed before it is intentionally prematurely terminated. As a result, the returned sample will
|
613 |
+
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
|
614 |
+
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
|
615 |
+
"Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
|
616 |
+
Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
|
617 |
+
guidance_scale (`float`, *optional*, defaults to 5.0):
|
618 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
619 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
620 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
621 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
622 |
+
usually at the expense of lower image quality.
|
623 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
624 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
625 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
626 |
+
less than `1`).
|
627 |
+
negative_prompt_2 (`str` or `List[str]`, *optional*):
|
628 |
+
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
629 |
+
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
|
630 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
631 |
+
The number of images to generate per prompt.
|
632 |
+
eta (`float`, *optional*, defaults to 0.0):
|
633 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
634 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
635 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
636 |
+
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
|
637 |
+
to make generation deterministic.
|
638 |
+
latents (`torch.FloatTensor`, *optional*):
|
639 |
+
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
|
640 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
641 |
+
tensor will ge generated by sampling using the supplied random `generator`.
|
642 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
643 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
644 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
645 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
646 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
647 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
648 |
+
argument.
|
649 |
+
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
650 |
+
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
651 |
+
If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
652 |
+
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
653 |
+
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
654 |
+
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
655 |
+
input argument.
|
656 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
657 |
+
The output format of the generate image. Choose between
|
658 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
659 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
660 |
+
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
|
661 |
+
of a plain tuple.
|
662 |
+
callback (`Callable`, *optional*):
|
663 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
664 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
665 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
666 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
667 |
+
called at every step.
|
668 |
+
cross_attention_kwargs (`dict`, *optional*):
|
669 |
+
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
|
670 |
+
`self.processor` in
|
671 |
+
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
672 |
+
guidance_rescale (`float`, *optional*, defaults to 0.7):
|
673 |
+
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
|
674 |
+
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
|
675 |
+
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
|
676 |
+
Guidance rescale factor should fix overexposure when using zero terminal SNR.
|
677 |
+
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
678 |
+
If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
|
679 |
+
`original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
|
680 |
+
explained in section 2.2 of
|
681 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
682 |
+
crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
|
683 |
+
`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
|
684 |
+
`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
|
685 |
+
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
|
686 |
+
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
687 |
+
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
|
688 |
+
For most cases, `target_size` should be set to the desired height and width of the generated image. If
|
689 |
+
not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
|
690 |
+
section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
|
691 |
+
|
692 |
+
Examples:
|
693 |
+
|
694 |
+
Returns:
|
695 |
+
[`~hotshot_xl.HotshotPipelineXLOutput`] or `tuple`:
|
696 |
+
[`~hotshot_xl.HotshotPipelineXLOutput`] if `return_dict` is True, otherwise a
|
697 |
+
`tuple`. When returning a tuple, the first element is a list with the generated images.
|
698 |
+
"""
|
699 |
+
self.low_vram_mode = low_vram_mode
|
700 |
+
|
701 |
+
if video_length > 1:
|
702 |
+
print(f"Warning - setting num_images_per_prompt = 1 because video_length = {video_length}")
|
703 |
+
num_images_per_prompt = 1
|
704 |
+
|
705 |
+
# 0. Default height and width to unet
|
706 |
+
height = height or self.default_sample_size * self.vae_scale_factor
|
707 |
+
width = width or self.default_sample_size * self.vae_scale_factor
|
708 |
+
|
709 |
+
original_size = original_size or (height, width)
|
710 |
+
target_size = target_size or (height, width)
|
711 |
+
|
712 |
+
# 1. Check inputs. Raise error if not correct
|
713 |
+
self.check_inputs(
|
714 |
+
prompt,
|
715 |
+
prompt_2,
|
716 |
+
height,
|
717 |
+
width,
|
718 |
+
callback_steps,
|
719 |
+
negative_prompt,
|
720 |
+
negative_prompt_2,
|
721 |
+
prompt_embeds,
|
722 |
+
negative_prompt_embeds,
|
723 |
+
pooled_prompt_embeds,
|
724 |
+
negative_pooled_prompt_embeds,
|
725 |
+
)
|
726 |
+
|
727 |
+
# 2. Define call parameters
|
728 |
+
if prompt is not None and isinstance(prompt, str):
|
729 |
+
batch_size = 1
|
730 |
+
elif prompt is not None and isinstance(prompt, list):
|
731 |
+
batch_size = len(prompt)
|
732 |
+
else:
|
733 |
+
batch_size = prompt_embeds.shape[0]
|
734 |
+
|
735 |
+
device = self._execution_device
|
736 |
+
|
737 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
738 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
739 |
+
# corresponds to doing no classifier free guidance.
|
740 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
741 |
+
|
742 |
+
if self.low_vram_mode:
|
743 |
+
self.text_encoder.to(device)
|
744 |
+
self.text_encoder_2.to(device)
|
745 |
+
|
746 |
+
# 3. Encode input prompt
|
747 |
+
text_encoder_lora_scale = (
|
748 |
+
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
749 |
+
)
|
750 |
+
(
|
751 |
+
prompt_embeds,
|
752 |
+
negative_prompt_embeds,
|
753 |
+
pooled_prompt_embeds,
|
754 |
+
negative_pooled_prompt_embeds,
|
755 |
+
) = self.encode_prompt(
|
756 |
+
prompt=prompt,
|
757 |
+
prompt_2=prompt_2,
|
758 |
+
device=device,
|
759 |
+
num_images_per_prompt=num_images_per_prompt,
|
760 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
761 |
+
negative_prompt=negative_prompt,
|
762 |
+
negative_prompt_2=negative_prompt_2,
|
763 |
+
prompt_embeds=prompt_embeds,
|
764 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
765 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
766 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
767 |
+
lora_scale=text_encoder_lora_scale,
|
768 |
+
)
|
769 |
+
|
770 |
+
if self.low_vram_mode:
|
771 |
+
self.text_encoder.to(torch.device("cpu"))
|
772 |
+
self.text_encoder_2.to(torch.device("cpu"))
|
773 |
+
self.vae.to(torch.device("cpu"))
|
774 |
+
torch.cuda.empty_cache()
|
775 |
+
torch.cuda.synchronize()
|
776 |
+
gc.collect()
|
777 |
+
|
778 |
+
# 4. Prepare timesteps
|
779 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
780 |
+
|
781 |
+
timesteps = self.scheduler.timesteps
|
782 |
+
|
783 |
+
# 5. Prepare latent variables
|
784 |
+
num_channels_latents = self.unet.config.in_channels
|
785 |
+
latents = self.prepare_latents(
|
786 |
+
batch_size * num_images_per_prompt,
|
787 |
+
num_channels_latents,
|
788 |
+
video_length,
|
789 |
+
height,
|
790 |
+
width,
|
791 |
+
prompt_embeds.dtype,
|
792 |
+
device,
|
793 |
+
generator,
|
794 |
+
latents,
|
795 |
+
)
|
796 |
+
|
797 |
+
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
798 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
799 |
+
|
800 |
+
# 7. Prepare added time ids & embeddings
|
801 |
+
add_text_embeds = pooled_prompt_embeds
|
802 |
+
add_time_ids = self._get_add_time_ids(
|
803 |
+
original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
|
804 |
+
)
|
805 |
+
|
806 |
+
# todo - negative_original_size from latest diffusers for cfg
|
807 |
+
|
808 |
+
if do_classifier_free_guidance:
|
809 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
|
810 |
+
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
|
811 |
+
add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
|
812 |
+
|
813 |
+
prompt_embeds = prompt_embeds.to(device)
|
814 |
+
add_text_embeds = add_text_embeds.to(device)
|
815 |
+
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
|
816 |
+
|
817 |
+
# 8. Denoising loop
|
818 |
+
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
819 |
+
|
820 |
+
# 7.1 Apply denoising_end
|
821 |
+
if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
|
822 |
+
discrete_timestep_cutoff = int(
|
823 |
+
round(
|
824 |
+
self.scheduler.config.num_train_timesteps
|
825 |
+
- (denoising_end * self.scheduler.config.num_train_timesteps)
|
826 |
+
)
|
827 |
+
)
|
828 |
+
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
|
829 |
+
timesteps = timesteps[:num_inference_steps]
|
830 |
+
|
831 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
832 |
+
for i, t in enumerate(timesteps):
|
833 |
+
# expand the latents if we are doing classifier free guidance
|
834 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
835 |
+
|
836 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
837 |
+
|
838 |
+
# predict the noise residual
|
839 |
+
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
|
840 |
+
noise_pred = self.unet(
|
841 |
+
latent_model_input,
|
842 |
+
t,
|
843 |
+
encoder_hidden_states=prompt_embeds,
|
844 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
845 |
+
added_cond_kwargs=added_cond_kwargs,
|
846 |
+
return_dict=False,
|
847 |
+
enable_temporal_attentions= video_length > 1
|
848 |
+
)[0]
|
849 |
+
|
850 |
+
# perform guidance
|
851 |
+
if do_classifier_free_guidance:
|
852 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
853 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
854 |
+
|
855 |
+
if do_classifier_free_guidance and guidance_rescale > 0.0:
|
856 |
+
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
857 |
+
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
|
858 |
+
|
859 |
+
# compute the previous noisy sample x_t -> x_t-1
|
860 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
861 |
+
|
862 |
+
# call the callback, if provided
|
863 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
864 |
+
progress_bar.update()
|
865 |
+
if callback is not None and i % callback_steps == 0:
|
866 |
+
callback(i, t, latents)
|
867 |
+
|
868 |
+
# make sure the VAE is in float32 mode, as it overflows in float16
|
869 |
+
if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
|
870 |
+
self.upcast_vae()
|
871 |
+
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
872 |
+
|
873 |
+
# if not output_type == "latent":
|
874 |
+
# image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
875 |
+
# else:
|
876 |
+
# image = latents
|
877 |
+
# return StableDiffusionXLPipelineOutput(images=image)
|
878 |
+
|
879 |
+
# apply watermark if available
|
880 |
+
# if self.watermark is not None:
|
881 |
+
# image = self.watermark.apply_watermark(image)
|
882 |
+
|
883 |
+
#image = self.image_processor.postprocess(image, output_type=output_type)
|
884 |
+
|
885 |
+
if self.low_vram_mode:
|
886 |
+
self.vae.to(device)
|
887 |
+
torch.cuda.empty_cache()
|
888 |
+
torch.cuda.synchronize()
|
889 |
+
gc.collect()
|
890 |
+
|
891 |
+
video = self.decode_latents(latents)
|
892 |
+
|
893 |
+
# Convert to tensor
|
894 |
+
if output_type == "tensor":
|
895 |
+
video = torch.from_numpy(video)
|
896 |
+
|
897 |
+
if not return_dict:
|
898 |
+
return video
|
899 |
+
|
900 |
+
return HotshotPipelineXLOutput(videos=video)
|
901 |
+
|
902 |
+
#
|
903 |
+
# # Offload last model to CPU
|
904 |
+
# if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
905 |
+
# self.final_offload_hook.offload()
|
906 |
+
#
|
907 |
+
# if not return_dict:
|
908 |
+
# return (image,)
|
909 |
+
#
|
910 |
+
# return StableDiffusionXLPipelineOutput(images=image)
|
911 |
+
|
912 |
+
# Overrride to properly handle the loading and unloading of the additional text encoder.
|
913 |
+
def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
|
914 |
+
# We could have accessed the unet config from `lora_state_dict()` too. We pass
|
915 |
+
# it here explicitly to be able to tell that it's coming from an SDXL
|
916 |
+
# pipeline.
|
917 |
+
state_dict, network_alphas = self.lora_state_dict(
|
918 |
+
pretrained_model_name_or_path_or_dict,
|
919 |
+
unet_config=self.unet.config,
|
920 |
+
**kwargs,
|
921 |
+
)
|
922 |
+
self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
|
923 |
+
|
924 |
+
text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
|
925 |
+
if len(text_encoder_state_dict) > 0:
|
926 |
+
self.load_lora_into_text_encoder(
|
927 |
+
text_encoder_state_dict,
|
928 |
+
network_alphas=network_alphas,
|
929 |
+
text_encoder=self.text_encoder,
|
930 |
+
prefix="text_encoder",
|
931 |
+
lora_scale=self.lora_scale,
|
932 |
+
)
|
933 |
+
|
934 |
+
text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
|
935 |
+
if len(text_encoder_2_state_dict) > 0:
|
936 |
+
self.load_lora_into_text_encoder(
|
937 |
+
text_encoder_2_state_dict,
|
938 |
+
network_alphas=network_alphas,
|
939 |
+
text_encoder=self.text_encoder_2,
|
940 |
+
prefix="text_encoder_2",
|
941 |
+
lora_scale=self.lora_scale,
|
942 |
+
)
|
943 |
+
|
944 |
+
@classmethod
|
945 |
+
def save_lora_weights(
|
946 |
+
self,
|
947 |
+
save_directory: Union[str, os.PathLike],
|
948 |
+
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
|
949 |
+
text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
|
950 |
+
text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
|
951 |
+
is_main_process: bool = True,
|
952 |
+
weight_name: str = None,
|
953 |
+
save_function: Callable = None,
|
954 |
+
safe_serialization: bool = False,
|
955 |
+
):
|
956 |
+
state_dict = {}
|
957 |
+
|
958 |
+
def pack_weights(layers, prefix):
|
959 |
+
layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
|
960 |
+
layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
|
961 |
+
return layers_state_dict
|
962 |
+
|
963 |
+
state_dict.update(pack_weights(unet_lora_layers, "unet"))
|
964 |
+
|
965 |
+
if text_encoder_lora_layers and text_encoder_2_lora_layers:
|
966 |
+
state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
|
967 |
+
state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
|
968 |
+
|
969 |
+
self.write_lora_layers(
|
970 |
+
state_dict=state_dict,
|
971 |
+
save_directory=save_directory,
|
972 |
+
is_main_process=is_main_process,
|
973 |
+
weight_name=weight_name,
|
974 |
+
save_function=save_function,
|
975 |
+
safe_serialization=safe_serialization,
|
976 |
+
)
|
977 |
+
|
978 |
+
def decode_latents(self, latents):
|
979 |
+
video_length = latents.shape[2]
|
980 |
+
latents = 1 / self.vae.config.scaling_factor * latents
|
981 |
+
latents = rearrange(latents, "b c f h w -> (b f) c h w")
|
982 |
+
# video = self.vae.decode(latents).sample
|
983 |
+
video = []
|
984 |
+
for frame_idx in tqdm(range(latents.shape[0])):
|
985 |
+
video.append(self.vae.decode(
|
986 |
+
latents[frame_idx:frame_idx+1]).sample)
|
987 |
+
video = torch.cat(video)
|
988 |
+
video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
|
989 |
+
video = (video / 2.0 + 0.5).clamp(0, 1)
|
990 |
+
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
|
991 |
+
video = video.cpu().float().numpy()
|
992 |
+
return video
|
993 |
+
|
994 |
+
def _remove_text_encoder_monkey_patch(self):
|
995 |
+
self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
|
996 |
+
self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
|