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•
2ad3800
1
Parent(s):
0017a3b
env
Browse files- app.py +1 -1
- extension.py +859 -0
- requirements.txt +2 -27
app.py
CHANGED
@@ -4,7 +4,7 @@ import torch
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import numpy as np
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import cv2
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from PIL import Image, ImageFilter
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-
from
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negative_prompt = ""
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device = torch.device('cuda')
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import numpy as np
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import cv2
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from PIL import Image, ImageFilter
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+
from extension import CustomStableDiffusionControlNetPipeline
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negative_prompt = ""
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device = torch.device('cuda')
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extension.py
ADDED
@@ -0,0 +1,859 @@
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1 |
+
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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2 |
+
import torch
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3 |
+
import numpy as np
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4 |
+
from typing import Any, Callable, Dict, List, Optional, Union
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5 |
+
import sys
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import PIL
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import os
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import inspect
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import diffusers
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+
path = inspect.getfile(diffusers)
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print(path)
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sys.path.append(os.path.join(path, "pipelines/controlnet"))
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+
sys.path.append(path)
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+
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
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+
from diffusers.utils import replace_example_docstring
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+
from diffusers.image_processor import PipelineImageInput
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+
from diffusers.utils.torch_utils import is_compiled_module
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+
from diffusers.loaders import TextualInversionLoaderMixin, LoraLoaderMixin
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19 |
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from diffusers.utils.peft_utils import unscale_lora_layers
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20 |
+
from diffusers.utils.import_utils import is_torch_version
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from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
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from diffusers.utils import PIL_INTERPOLATION
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EXAMPLE_DOC_STRING = ""
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USE_PEFT_BACKEND = False
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25 |
+
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26 |
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class CustomStableDiffusionControlNetPipeline(StableDiffusionControlNetPipeline):
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def encode_prompt(
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self,
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prompt,
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device,
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+
num_images_per_prompt,
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+
do_classifier_free_guidance,
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+
negative_prompt=None,
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+
prompt_embeds: Optional[torch.FloatTensor] = None,
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+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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lora_scale: Optional[float] = None,
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clip_skip: Optional[int] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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+
num_images_per_prompt (`int`):
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48 |
+
number of images that should be generated per prompt
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49 |
+
do_classifier_free_guidance (`bool`):
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+
whether to use classifier free guidance or not
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+
negative_prompt (`str` or `List[str]`, *optional*):
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52 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
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+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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+
prompt_embeds (`torch.FloatTensor`, *optional*):
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56 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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57 |
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provided, text embeddings will be generated from `prompt` input argument.
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58 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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59 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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60 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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61 |
+
argument.
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62 |
+
lora_scale (`float`, *optional*):
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63 |
+
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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64 |
+
clip_skip (`int`, *optional*):
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+
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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the output of the pre-final layer will be used for computing the prompt embeddings.
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"""
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68 |
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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70 |
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if lora_scale is not None and isinstance(self, LoraLoaderMixin):
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71 |
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self._lora_scale = lora_scale
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# dynamically adjust the LoRA scale
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74 |
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if not USE_PEFT_BACKEND:
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adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
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else:
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scale_lora_layers(self.text_encoder, lora_scale)
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+
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79 |
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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81 |
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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83 |
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else:
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batch_size = prompt_embeds.shape[0]
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+
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if prompt_embeds is None:
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# textual inversion: procecss multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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+
text_input_ids = text_inputs.input_ids
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+
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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100 |
+
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101 |
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
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text_input_ids, untruncated_ids
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):
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removed_text = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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107 |
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logger.warning(
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108 |
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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110 |
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)
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111 |
+
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112 |
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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113 |
+
attention_mask = text_inputs.attention_mask.to(device)
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114 |
+
else:
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115 |
+
attention_mask = None
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116 |
+
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117 |
+
if clip_skip is None:
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118 |
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prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
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119 |
+
prompt_embeds = prompt_embeds[0]
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120 |
+
else:
|
121 |
+
prompt_embeds = self.text_encoder(
|
122 |
+
text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
|
123 |
+
)
|
124 |
+
# Access the `hidden_states` first, that contains a tuple of
|
125 |
+
# all the hidden states from the encoder layers. Then index into
|
126 |
+
# the tuple to access the hidden states from the desired layer.
|
127 |
+
prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
|
128 |
+
# We also need to apply the final LayerNorm here to not mess with the
|
129 |
+
# representations. The `last_hidden_states` that we typically use for
|
130 |
+
# obtaining the final prompt representations passes through the LayerNorm
|
131 |
+
# layer.
|
132 |
+
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
|
133 |
+
|
134 |
+
if self.text_encoder is not None:
|
135 |
+
prompt_embeds_dtype = self.text_encoder.dtype
|
136 |
+
elif self.unet is not None:
|
137 |
+
prompt_embeds_dtype = self.unet.dtype
|
138 |
+
else:
|
139 |
+
prompt_embeds_dtype = prompt_embeds.dtype
|
140 |
+
|
141 |
+
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
142 |
+
|
143 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
144 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
145 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
146 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
147 |
+
|
148 |
+
# get unconditional embeddings for classifier free guidance
|
149 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
150 |
+
uncond_tokens: List[str]
|
151 |
+
if negative_prompt is None:
|
152 |
+
uncond_tokens = [""] * batch_size
|
153 |
+
elif prompt is not None and type(prompt) is not type(negative_prompt):
|
154 |
+
raise TypeError(
|
155 |
+
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
156 |
+
f" {type(prompt)}."
|
157 |
+
)
|
158 |
+
elif isinstance(negative_prompt, str):
|
159 |
+
uncond_tokens = [negative_prompt]
|
160 |
+
elif batch_size != len(negative_prompt):
|
161 |
+
raise ValueError(
|
162 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
163 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
164 |
+
" the batch size of `prompt`."
|
165 |
+
)
|
166 |
+
else:
|
167 |
+
uncond_tokens = negative_prompt
|
168 |
+
|
169 |
+
# textual inversion: procecss multi-vector tokens if necessary
|
170 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
171 |
+
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
172 |
+
|
173 |
+
max_length = prompt_embeds.shape[1]
|
174 |
+
uncond_input = self.tokenizer(
|
175 |
+
uncond_tokens,
|
176 |
+
padding="max_length",
|
177 |
+
max_length=max_length,
|
178 |
+
truncation=True,
|
179 |
+
return_tensors="pt",
|
180 |
+
)
|
181 |
+
|
182 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
183 |
+
attention_mask = uncond_input.attention_mask.to(device)
|
184 |
+
else:
|
185 |
+
attention_mask = None
|
186 |
+
|
187 |
+
negative_prompt_embeds = self.text_encoder(
|
188 |
+
uncond_input.input_ids.to(device),
|
189 |
+
attention_mask=attention_mask,
|
190 |
+
)
|
191 |
+
negative_prompt_embeds = negative_prompt_embeds[0]
|
192 |
+
|
193 |
+
if do_classifier_free_guidance:
|
194 |
+
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
195 |
+
seq_len = negative_prompt_embeds.shape[1]
|
196 |
+
|
197 |
+
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
198 |
+
|
199 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
200 |
+
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
201 |
+
|
202 |
+
if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
|
203 |
+
# Retrieve the original scale by scaling back the LoRA layers
|
204 |
+
unscale_lora_layers(self.text_encoder, lora_scale)
|
205 |
+
# print(prompt_embeds.shape, negative_prompt_embeds.shape)
|
206 |
+
return prompt_embeds, negative_prompt_embeds
|
207 |
+
|
208 |
+
def prepare_latents_legacy(
|
209 |
+
self,
|
210 |
+
image,
|
211 |
+
timestep,
|
212 |
+
batch_size,
|
213 |
+
num_images_per_prompt,
|
214 |
+
dtype,
|
215 |
+
device,
|
216 |
+
generator,
|
217 |
+
):
|
218 |
+
image = image.to(device=self.device, dtype=dtype)
|
219 |
+
init_latent_dist = self.vae.encode(image).latent_dist
|
220 |
+
init_latents = init_latent_dist.sample(generator=generator)
|
221 |
+
init_latents = 0.18215 * init_latents
|
222 |
+
|
223 |
+
# Expand init_latents for batch_size and num_images_per_prompt
|
224 |
+
init_latents = torch.cat(
|
225 |
+
[init_latents] * batch_size * num_images_per_prompt, dim=0
|
226 |
+
)
|
227 |
+
init_latents_orig = init_latents
|
228 |
+
|
229 |
+
# add noise to latents using the timesteps
|
230 |
+
noise = torch.randn(
|
231 |
+
init_latents.shape, generator=generator, device=self.device, dtype=dtype
|
232 |
+
)
|
233 |
+
print(init_latents.shape, noise.shape)
|
234 |
+
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
|
235 |
+
latents = init_latents
|
236 |
+
return latents, init_latents_orig, noise
|
237 |
+
|
238 |
+
def preprocess_mask(self, mask, scale_factor=8):
|
239 |
+
mask = mask.convert("L")
|
240 |
+
w, h = mask.size
|
241 |
+
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
242 |
+
mask = mask.resize(
|
243 |
+
(w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]
|
244 |
+
)
|
245 |
+
# print("in preprocess mask 0", np.unique(mask))
|
246 |
+
mask = np.array(mask).astype(np.float32) / 255.0
|
247 |
+
mask = np.tile(mask, (4, 1, 1))
|
248 |
+
# print("in preprocess mask 1", np.unique(mask))
|
249 |
+
mask = mask[None].transpose(0, 1, 2, 3)
|
250 |
+
mask = 1 - mask # repaint white, keep black
|
251 |
+
# print("in preprocess mask 2", np.unique(mask))
|
252 |
+
mask = torch.from_numpy(mask)
|
253 |
+
return mask
|
254 |
+
|
255 |
+
def preprocess_image(self, image):
|
256 |
+
w, h = image.size
|
257 |
+
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
|
258 |
+
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
|
259 |
+
image = np.array(image).astype(np.float32) / 255.0
|
260 |
+
image = image[None].transpose(0, 3, 1, 2)
|
261 |
+
image = torch.from_numpy(image)
|
262 |
+
return 2.0 * image - 1.0
|
263 |
+
|
264 |
+
def get_timesteps(self, num_inference_steps, strength, device):
|
265 |
+
# get the original timestep using init_timestep
|
266 |
+
offset = self.scheduler.config.get("steps_offset", 0)
|
267 |
+
init_timestep = int(num_inference_steps * strength) + offset
|
268 |
+
init_timestep = min(init_timestep, num_inference_steps)
|
269 |
+
|
270 |
+
t_start = max(num_inference_steps - init_timestep + offset, 0)
|
271 |
+
timesteps = self.scheduler.timesteps[t_start:]
|
272 |
+
|
273 |
+
return timesteps, num_inference_steps - t_start
|
274 |
+
|
275 |
+
@torch.no_grad()
|
276 |
+
def collage(
|
277 |
+
self,
|
278 |
+
prompt: Union[str, List[str]],
|
279 |
+
image: Union[torch.FloatTensor, PIL.Image.Image],
|
280 |
+
mask_image: Union[torch.FloatTensor, PIL.Image.Image],
|
281 |
+
strength: float = 0.8,
|
282 |
+
num_inference_steps: Optional[int] = 50,
|
283 |
+
guidance_scale: Optional[float] = 7.5,
|
284 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
285 |
+
num_images_per_prompt: Optional[int] = 1,
|
286 |
+
eta: Optional[float] = 0.0,
|
287 |
+
generator: Optional[torch.Generator] = None,
|
288 |
+
output_type: Optional[str] = "pil",
|
289 |
+
return_dict: bool = True,
|
290 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
291 |
+
callback_steps: Optional[int] = 1,
|
292 |
+
attention_mod: Optional[Callable] = None,
|
293 |
+
**kwargs,
|
294 |
+
):
|
295 |
+
r"""
|
296 |
+
Function invoked when calling the pipeline for generation.
|
297 |
+
Hello
|
298 |
+
|
299 |
+
Args:
|
300 |
+
prompt (`str` or `List[str]`):
|
301 |
+
The prompt or prompts to guide the image generation.
|
302 |
+
image (`torch.FloatTensor` or `PIL.Image.Image`):
|
303 |
+
`Image`, or tensor representing an image batch, that will be used as the starting point for the
|
304 |
+
process. This is the image whose masked region will be inpainted.
|
305 |
+
mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
|
306 |
+
`Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
|
307 |
+
replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
|
308 |
+
PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
|
309 |
+
contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
|
310 |
+
strength (`float`, *optional*, defaults to 0.8):
|
311 |
+
Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
|
312 |
+
is 1, the denoising process will be run on the masked area for the full number of iterations specified
|
313 |
+
in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more noise to
|
314 |
+
that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
|
315 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
316 |
+
The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
|
317 |
+
the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
|
318 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
319 |
+
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
|
320 |
+
`guidance_scale` is defined as `w` of equation 2. of [Imagen
|
321 |
+
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
|
322 |
+
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
|
323 |
+
usually at the expense of lower image quality.
|
324 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
325 |
+
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
|
326 |
+
if `guidance_scale` is less than `1`).
|
327 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
328 |
+
The number of images to generate per prompt.
|
329 |
+
eta (`float`, *optional*, defaults to 0.0):
|
330 |
+
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
|
331 |
+
[`schedulers.DDIMScheduler`], will be ignored for others.
|
332 |
+
generator (`torch.Generator`, *optional*):
|
333 |
+
A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
|
334 |
+
deterministic.
|
335 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
336 |
+
The output format of the generate image. Choose between
|
337 |
+
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
338 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
339 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
340 |
+
plain tuple.
|
341 |
+
callback (`Callable`, *optional*):
|
342 |
+
A function that will be called every `callback_steps` steps during inference. The function will be
|
343 |
+
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
344 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
345 |
+
The frequency at which the `callback` function will be called. If not specified, the callback will be
|
346 |
+
called at every step.
|
347 |
+
|
348 |
+
Returns:
|
349 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
350 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
|
351 |
+
When returning a tuple, the first element is a list with the generated images, and the second element is a
|
352 |
+
list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
|
353 |
+
(nsfw) content, according to the `safety_checker`.
|
354 |
+
"""
|
355 |
+
# message = "Please use `image` instead of `init_image`."
|
356 |
+
# init_image = deprecate("init_image", "0.12.0", message, take_from=kwargs)
|
357 |
+
# image = init_image or image
|
358 |
+
|
359 |
+
# 1. Check inputs
|
360 |
+
# self.check_inputs(prompt, strength, callback_steps)
|
361 |
+
|
362 |
+
# 2. Define call parameters
|
363 |
+
batch_size = 1
|
364 |
+
device = self._execution_device
|
365 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
366 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
367 |
+
# corresponds to doing no classifier free guidance.
|
368 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
369 |
+
|
370 |
+
# 3. Encode input prompt
|
371 |
+
text_embeddings = self._encode_prompt(
|
372 |
+
prompt,
|
373 |
+
device,
|
374 |
+
num_images_per_prompt,
|
375 |
+
do_classifier_free_guidance,
|
376 |
+
negative_prompt,
|
377 |
+
)
|
378 |
+
|
379 |
+
# 4. Preprocess image and mask
|
380 |
+
if not isinstance(image[0], torch.FloatTensor):
|
381 |
+
image = torch.cat([self.preprocess_image(image[i]) for i in range(len(image))], dim=0)
|
382 |
+
|
383 |
+
if not isinstance(mask_image, torch.FloatTensor):
|
384 |
+
mask_image = torch.cat([self.preprocess_mask(mask_image[i], self.vae_scale_factor) for i in range(len(mask_image))], dim=0)
|
385 |
+
|
386 |
+
# 5. set timesteps
|
387 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
388 |
+
timesteps, num_inference_steps = self.get_timesteps(
|
389 |
+
num_inference_steps, strength, device
|
390 |
+
)
|
391 |
+
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
|
392 |
+
|
393 |
+
# 6. Prepare latent variables
|
394 |
+
# encode the init image into latents and scale the latents
|
395 |
+
latents, init_latents_orig, noise = self.prepare_latents_legacy(
|
396 |
+
image,
|
397 |
+
latent_timestep,
|
398 |
+
batch_size,
|
399 |
+
num_images_per_prompt,
|
400 |
+
text_embeddings.dtype,
|
401 |
+
device,
|
402 |
+
generator,
|
403 |
+
)
|
404 |
+
|
405 |
+
# 7. Prepare mask latent
|
406 |
+
mask = mask_image.to(device=self.device, dtype=latents.dtype)
|
407 |
+
mask = torch.cat([mask] * batch_size * num_images_per_prompt)
|
408 |
+
|
409 |
+
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
410 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
411 |
+
|
412 |
+
# 9. Denoising loop
|
413 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
414 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
415 |
+
for i, t in enumerate(timesteps):
|
416 |
+
# expand the latents if we are doing classifier free guidance
|
417 |
+
latent_model_input = (
|
418 |
+
torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
419 |
+
)
|
420 |
+
latent_model_input = self.scheduler.scale_model_input(
|
421 |
+
latent_model_input, t
|
422 |
+
)
|
423 |
+
|
424 |
+
if attention_mod is not None:
|
425 |
+
sigma = self.scheduler.sigmas[i]
|
426 |
+
attention_mod(self.unet, sigma)
|
427 |
+
|
428 |
+
# predict the noise residual
|
429 |
+
noise_pred = self.unet(
|
430 |
+
latent_model_input, t, encoder_hidden_states=text_embeddings
|
431 |
+
).sample
|
432 |
+
|
433 |
+
# perform guidance
|
434 |
+
if do_classifier_free_guidance:
|
435 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
436 |
+
noise_pred = noise_pred_uncond + guidance_scale * (
|
437 |
+
noise_pred_text - noise_pred_uncond
|
438 |
+
)
|
439 |
+
# compute the previous noisy sample x_t -> x_t-1
|
440 |
+
latents = self.scheduler.step(
|
441 |
+
noise_pred, t, latents, **extra_step_kwargs
|
442 |
+
).prev_sample
|
443 |
+
# masking
|
444 |
+
|
445 |
+
noise = torch.randn(
|
446 |
+
latents.shape,
|
447 |
+
generator=generator,
|
448 |
+
device=self.device,
|
449 |
+
dtype=text_embeddings.dtype,
|
450 |
+
)
|
451 |
+
init_latents_proper = self.scheduler.add_noise(
|
452 |
+
init_latents_orig, noise, torch.tensor([t])
|
453 |
+
)
|
454 |
+
|
455 |
+
mask_t = (mask > (1 - t/1000)).type(mask.dtype) # So when t is high, most of the mask is 1 (fixed), but when t is low, most of the mask is 0 (variable)
|
456 |
+
dilate_size = (int)(4*t/1000)
|
457 |
+
mask_t_dilated = torch.nn.functional.max_pool2d(mask_t, dilate_size*2 + 1, stride=1, padding=dilate_size)
|
458 |
+
latents = (init_latents_proper * mask_t_dilated) + (latents * (1 - mask_t_dilated))
|
459 |
+
|
460 |
+
# call the callback, if provided
|
461 |
+
if i == len(timesteps) - 1 or (
|
462 |
+
(i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
|
463 |
+
):
|
464 |
+
progress_bar.update()
|
465 |
+
if callback is not None and i % callback_steps == 0:
|
466 |
+
callback(i, t, latents)
|
467 |
+
|
468 |
+
# 10. Post-processing
|
469 |
+
image = self.decode_latents(latents)
|
470 |
+
|
471 |
+
# 11. Run safety checker
|
472 |
+
image, has_nsfw_concept = self.run_safety_checker(
|
473 |
+
image, device, text_embeddings.dtype
|
474 |
+
)
|
475 |
+
|
476 |
+
# print(image.shape)
|
477 |
+
|
478 |
+
# 12. Convert to PIL
|
479 |
+
if output_type == "pil":
|
480 |
+
image = self.numpy_to_pil(image)
|
481 |
+
|
482 |
+
if not return_dict:
|
483 |
+
return (image, has_nsfw_concept)
|
484 |
+
|
485 |
+
return StableDiffusionPipelineOutput(
|
486 |
+
images=image, nsfw_content_detected=has_nsfw_concept
|
487 |
+
)
|
488 |
+
|
489 |
+
@torch.no_grad()
|
490 |
+
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
491 |
+
def __call__(
|
492 |
+
self,
|
493 |
+
prompt: Union[str, List[str]] = None,
|
494 |
+
image: PipelineImageInput = None,
|
495 |
+
height: Optional[int] = None,
|
496 |
+
width: Optional[int] = None,
|
497 |
+
num_inference_steps: int = 50,
|
498 |
+
guidance_scale: float = 7.5,
|
499 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
500 |
+
num_images_per_prompt: Optional[int] = 1,
|
501 |
+
eta: float = 0.0,
|
502 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
503 |
+
latents: Optional[torch.FloatTensor] = None,
|
504 |
+
prompt_embeds: Optional[torch.FloatTensor] = None,
|
505 |
+
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
|
506 |
+
output_type: Optional[str] = "pil",
|
507 |
+
return_dict: bool = True,
|
508 |
+
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
|
509 |
+
callback_steps: int = 1,
|
510 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
511 |
+
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
512 |
+
guess_mode: bool = False,
|
513 |
+
control_guidance_start: Union[float, List[float]] = 0.0,
|
514 |
+
control_guidance_end: Union[float, List[float]] = 1.0,
|
515 |
+
clip_skip: Optional[int] = None,
|
516 |
+
key_image = None,
|
517 |
+
key_scale = 0.5,
|
518 |
+
neg_mask = None,
|
519 |
+
neg_prompt = None,
|
520 |
+
):
|
521 |
+
r"""
|
522 |
+
The call function to the pipeline for generation.
|
523 |
+
|
524 |
+
Args:
|
525 |
+
prompt (`str` or `List[str]`, *optional*):
|
526 |
+
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
527 |
+
image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
|
528 |
+
`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
|
529 |
+
The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
|
530 |
+
specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
|
531 |
+
accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
|
532 |
+
and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
|
533 |
+
`init`, images must be passed as a list such that each element of the list can be correctly batched for
|
534 |
+
input to a single ControlNet.
|
535 |
+
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
536 |
+
The height in pixels of the generated image.
|
537 |
+
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
538 |
+
The width in pixels of the generated image.
|
539 |
+
num_inference_steps (`int`, *optional*, defaults to 50):
|
540 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
541 |
+
expense of slower inference.
|
542 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
543 |
+
A higher guidance scale value encourages the model to generate images closely linked to the text
|
544 |
+
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
545 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
546 |
+
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
547 |
+
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
548 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
549 |
+
The number of images to generate per prompt.
|
550 |
+
eta (`float`, *optional*, defaults to 0.0):
|
551 |
+
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
552 |
+
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
553 |
+
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
|
554 |
+
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
555 |
+
generation deterministic.
|
556 |
+
latents (`torch.FloatTensor`, *optional*):
|
557 |
+
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
558 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
559 |
+
tensor is generated by sampling using the supplied random `generator`.
|
560 |
+
prompt_embeds (`torch.FloatTensor`, *optional*):
|
561 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
562 |
+
provided, text embeddings are generated from the `prompt` input argument.
|
563 |
+
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
|
564 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
565 |
+
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
566 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
567 |
+
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
568 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
569 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
570 |
+
plain tuple.
|
571 |
+
callback (`Callable`, *optional*):
|
572 |
+
A function that calls every `callback_steps` steps during inference. The function is called with the
|
573 |
+
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
574 |
+
callback_steps (`int`, *optional*, defaults to 1):
|
575 |
+
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
576 |
+
every step.
|
577 |
+
cross_attention_kwargs (`dict`, *optional*):
|
578 |
+
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
579 |
+
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
580 |
+
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
581 |
+
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
|
582 |
+
to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
|
583 |
+
the corresponding scale as a list.
|
584 |
+
guess_mode (`bool`, *optional*, defaults to `False`):
|
585 |
+
The ControlNet encoder tries to recognize the content of the input image even if you remove all
|
586 |
+
prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
|
587 |
+
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
|
588 |
+
The percentage of total steps at which the ControlNet starts applying.
|
589 |
+
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
|
590 |
+
The percentage of total steps at which the ControlNet stops applying.
|
591 |
+
clip_skip (`int`, *optional*):
|
592 |
+
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
|
593 |
+
the output of the pre-final layer will be used for computing the prompt embeddings.
|
594 |
+
|
595 |
+
Examples:
|
596 |
+
|
597 |
+
Returns:
|
598 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
599 |
+
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
600 |
+
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
601 |
+
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
602 |
+
"not-safe-for-work" (nsfw) content.
|
603 |
+
"""
|
604 |
+
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
|
605 |
+
|
606 |
+
# align format for control guidance
|
607 |
+
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
|
608 |
+
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
|
609 |
+
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
|
610 |
+
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
|
611 |
+
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
|
612 |
+
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
|
613 |
+
control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
|
614 |
+
control_guidance_end
|
615 |
+
]
|
616 |
+
|
617 |
+
# 1. Check inputs. Raise error if not correct
|
618 |
+
self.check_inputs(
|
619 |
+
prompt,
|
620 |
+
image,
|
621 |
+
callback_steps,
|
622 |
+
negative_prompt,
|
623 |
+
prompt_embeds,
|
624 |
+
negative_prompt_embeds,
|
625 |
+
controlnet_conditioning_scale,
|
626 |
+
control_guidance_start,
|
627 |
+
control_guidance_end,
|
628 |
+
)
|
629 |
+
|
630 |
+
# 2. Define call parameters
|
631 |
+
if prompt is not None and isinstance(prompt, str):
|
632 |
+
batch_size = 1
|
633 |
+
elif prompt is not None and isinstance(prompt, list):
|
634 |
+
batch_size = len(prompt)
|
635 |
+
else:
|
636 |
+
batch_size = prompt_embeds.shape[0]
|
637 |
+
|
638 |
+
device = self._execution_device
|
639 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
640 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
641 |
+
# corresponds to doing no classifier free guidance.
|
642 |
+
do_classifier_free_guidance = guidance_scale > 1.0
|
643 |
+
|
644 |
+
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
|
645 |
+
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
|
646 |
+
|
647 |
+
global_pool_conditions = (
|
648 |
+
controlnet.config.global_pool_conditions
|
649 |
+
if isinstance(controlnet, ControlNetModel)
|
650 |
+
else controlnet.nets[0].config.global_pool_conditions
|
651 |
+
)
|
652 |
+
guess_mode = guess_mode or global_pool_conditions
|
653 |
+
|
654 |
+
# 3. Encode input prompt
|
655 |
+
text_encoder_lora_scale = (
|
656 |
+
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
657 |
+
)
|
658 |
+
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
659 |
+
prompt,
|
660 |
+
device,
|
661 |
+
num_images_per_prompt,
|
662 |
+
do_classifier_free_guidance,
|
663 |
+
negative_prompt,
|
664 |
+
lora_scale=text_encoder_lora_scale,
|
665 |
+
clip_skip=clip_skip,
|
666 |
+
)
|
667 |
+
# For classifier free guidance, we need to do two forward passes.
|
668 |
+
# Here we concatenate the unconditional and text embeddings into a single batch
|
669 |
+
# to avoid doing two forward passes
|
670 |
+
if do_classifier_free_guidance:
|
671 |
+
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
672 |
+
if neg_mask is not None:
|
673 |
+
empty_prompt_embeds = torch.cat([empty_negative_prompt_embeds, empty_prompt_embeds])
|
674 |
+
|
675 |
+
# 4. Prepare image
|
676 |
+
if isinstance(controlnet, ControlNetModel):
|
677 |
+
# breakpoint()
|
678 |
+
image = self.prepare_image(
|
679 |
+
image=image,
|
680 |
+
width=width,
|
681 |
+
height=height,
|
682 |
+
batch_size=batch_size * num_images_per_prompt,
|
683 |
+
num_images_per_prompt=num_images_per_prompt,
|
684 |
+
device=device,
|
685 |
+
dtype=controlnet.dtype,
|
686 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
687 |
+
guess_mode=guess_mode,
|
688 |
+
)
|
689 |
+
height, width = image.shape[-2:]
|
690 |
+
if key_image is not None:
|
691 |
+
key_image = self.prepare_image(
|
692 |
+
image=key_image,
|
693 |
+
width=width,
|
694 |
+
height=height,
|
695 |
+
batch_size=batch_size * num_images_per_prompt,
|
696 |
+
num_images_per_prompt=num_images_per_prompt,
|
697 |
+
device=device,
|
698 |
+
dtype=controlnet.dtype,
|
699 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
700 |
+
guess_mode=guess_mode,
|
701 |
+
)
|
702 |
+
elif isinstance(controlnet, MultiControlNetModel):
|
703 |
+
images = []
|
704 |
+
key_images = []
|
705 |
+
|
706 |
+
for image_ in image:
|
707 |
+
image_ = self.prepare_image(
|
708 |
+
image=image_,
|
709 |
+
width=width,
|
710 |
+
height=height,
|
711 |
+
batch_size=batch_size * num_images_per_prompt,
|
712 |
+
num_images_per_prompt=num_images_per_prompt,
|
713 |
+
device=device,
|
714 |
+
dtype=controlnet.dtype,
|
715 |
+
do_classifier_free_guidance=do_classifier_free_guidance,
|
716 |
+
guess_mode=guess_mode,
|
717 |
+
)
|
718 |
+
|
719 |
+
images.append(image_)
|
720 |
+
|
721 |
+
image = images
|
722 |
+
height, width = image[0].shape[-2:]
|
723 |
+
else:
|
724 |
+
assert False
|
725 |
+
|
726 |
+
# 5. Prepare timesteps
|
727 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
728 |
+
timesteps = self.scheduler.timesteps
|
729 |
+
|
730 |
+
# 6. Prepare latent variables
|
731 |
+
num_channels_latents = self.unet.config.in_channels
|
732 |
+
latents = self.prepare_latents(
|
733 |
+
batch_size * num_images_per_prompt,
|
734 |
+
num_channels_latents,
|
735 |
+
height,
|
736 |
+
width,
|
737 |
+
prompt_embeds.dtype,
|
738 |
+
device,
|
739 |
+
generator,
|
740 |
+
latents,
|
741 |
+
)
|
742 |
+
|
743 |
+
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
744 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
745 |
+
|
746 |
+
# 7.1 Create tensor stating which controlnets to keep
|
747 |
+
controlnet_keep = []
|
748 |
+
for i in range(len(timesteps)):
|
749 |
+
keeps = [
|
750 |
+
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
|
751 |
+
for s, e in zip(control_guidance_start, control_guidance_end)
|
752 |
+
]
|
753 |
+
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
|
754 |
+
|
755 |
+
|
756 |
+
# 8. Denoising loop
|
757 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
758 |
+
is_unet_compiled = is_compiled_module(self.unet)
|
759 |
+
is_controlnet_compiled = is_compiled_module(self.controlnet)
|
760 |
+
is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1")
|
761 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
762 |
+
for i, t in enumerate(timesteps):
|
763 |
+
# Relevant thread:
|
764 |
+
# https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428
|
765 |
+
if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1:
|
766 |
+
torch._inductor.cudagraph_mark_step_begin()
|
767 |
+
# expand the latents if we are doing classifier free guidance
|
768 |
+
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
|
769 |
+
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
770 |
+
|
771 |
+
# controlnet(s) inference
|
772 |
+
if guess_mode and do_classifier_free_guidance:
|
773 |
+
# Infer ControlNet only for the conditional batch.
|
774 |
+
control_model_input = latents
|
775 |
+
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
|
776 |
+
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
777 |
+
else:
|
778 |
+
control_model_input = latent_model_input
|
779 |
+
controlnet_prompt_embeds = prompt_embeds
|
780 |
+
|
781 |
+
if isinstance(controlnet_keep[i], list):
|
782 |
+
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
|
783 |
+
else:
|
784 |
+
controlnet_cond_scale = controlnet_conditioning_scale
|
785 |
+
if isinstance(controlnet_cond_scale, list):
|
786 |
+
controlnet_cond_scale = controlnet_cond_scale[0]
|
787 |
+
cond_scale = controlnet_cond_scale * controlnet_keep[i]
|
788 |
+
|
789 |
+
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
790 |
+
control_model_input,
|
791 |
+
t,
|
792 |
+
encoder_hidden_states=controlnet_prompt_embeds,
|
793 |
+
controlnet_cond=image,
|
794 |
+
conditioning_scale=cond_scale,
|
795 |
+
guess_mode=guess_mode,
|
796 |
+
return_dict=False,
|
797 |
+
)
|
798 |
+
|
799 |
+
if guess_mode and do_classifier_free_guidance:
|
800 |
+
# Infered ControlNet only for the conditional batch.
|
801 |
+
# To apply the output of ControlNet to both the unconditional and conditional batches,
|
802 |
+
# add 0 to the unconditional batch to keep it unchanged.
|
803 |
+
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
|
804 |
+
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
|
805 |
+
|
806 |
+
# predict the noise residual
|
807 |
+
noise_pred = self.unet(
|
808 |
+
latent_model_input,
|
809 |
+
t,
|
810 |
+
encoder_hidden_states=prompt_embeds,
|
811 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
812 |
+
down_block_additional_residuals=down_block_res_samples,
|
813 |
+
mid_block_additional_residual=mid_block_res_sample,
|
814 |
+
return_dict=False,
|
815 |
+
)[0]
|
816 |
+
|
817 |
+
# perform guidance
|
818 |
+
if do_classifier_free_guidance:
|
819 |
+
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
820 |
+
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
821 |
+
|
822 |
+
# compute the previous noisy sample x_t -> x_t-1
|
823 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
824 |
+
|
825 |
+
# call the callback, if provided
|
826 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
827 |
+
progress_bar.update()
|
828 |
+
if callback is not None and i % callback_steps == 0:
|
829 |
+
step_idx = i // getattr(self.scheduler, "order", 1)
|
830 |
+
callback(step_idx, t, latents)
|
831 |
+
|
832 |
+
# If we do sequential model offloading, let's offload unet and controlnet
|
833 |
+
# manually for max memory savings
|
834 |
+
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
835 |
+
self.unet.to("cpu")
|
836 |
+
self.controlnet.to("cpu")
|
837 |
+
torch.cuda.empty_cache()
|
838 |
+
|
839 |
+
if not output_type == "latent":
|
840 |
+
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
841 |
+
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
842 |
+
else:
|
843 |
+
image = latents
|
844 |
+
has_nsfw_concept = None
|
845 |
+
|
846 |
+
if has_nsfw_concept is None:
|
847 |
+
do_denormalize = [True] * image.shape[0]
|
848 |
+
else:
|
849 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
850 |
+
|
851 |
+
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
852 |
+
|
853 |
+
# Offload all models
|
854 |
+
self.maybe_free_model_hooks()
|
855 |
+
|
856 |
+
if not return_dict:
|
857 |
+
return (image, has_nsfw_concept)
|
858 |
+
|
859 |
+
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
requirements.txt
CHANGED
@@ -1,33 +1,8 @@
|
|
1 |
-
requests==2.31.0
|
2 |
-
setuptools==68.2.2
|
3 |
-
yaml==0.2.5
|
4 |
-
accelerate==0.24.1
|
5 |
diffusers==0.23.1
|
6 |
-
einops==0.3.0
|
7 |
-
flask==3.0.0
|
8 |
-
flask-cors==4.0.0
|
9 |
gradio==3.48.0
|
10 |
-
gradio-client==0.6.1
|
11 |
-
imageio==2.9.0
|
12 |
-
imageio-ffmpeg==0.4.2
|
13 |
-
matplotlib==3.7.3
|
14 |
-
multiprocess==0.70.15
|
15 |
-
omegaconf==2.3.0
|
16 |
-
opencv-contrib-python==4.3.0.36
|
17 |
-
opencv-python==4.8.1.78
|
18 |
-
opencv-python-headless==4.8.1.78
|
19 |
pillow==9.4.0
|
20 |
-
pytorch-lightning==1.5.0
|
21 |
-
safetensors==0.4.0
|
22 |
-
scikit-image==0.20.0
|
23 |
-
scikit-learn==1.3.1
|
24 |
-
scipy==1.9.1
|
25 |
-
threadpoolctl==3.2.0
|
26 |
-
tokenizers==0.14.1
|
27 |
torch==2.1.1
|
28 |
torchmetrics==0.6.0
|
29 |
torchvision==0.16.1
|
30 |
-
|
31 |
-
|
32 |
-
watchdog==3.0.0
|
33 |
-
open-clip-torch==2.0.2
|
|
|
|
|
|
|
|
|
|
|
1 |
diffusers==0.23.1
|
|
|
|
|
|
|
2 |
gradio==3.48.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
pillow==9.4.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
4 |
torch==2.1.1
|
5 |
torchmetrics==0.6.0
|
6 |
torchvision==0.16.1
|
7 |
+
numpy
|
8 |
+
inspect
|
|
|
|