Spaces:
Running
on
Zero
Running
on
Zero
zhiweili
commited on
Commit
•
e581b1e
1
Parent(s):
f01a424
test p2p
Browse files- app_haircolor_pix2pix.py +19 -3
- pipelines/pipeline_sd_adapter_p2p.py +1034 -0
app_haircolor_pix2pix.py
CHANGED
@@ -10,8 +10,10 @@ from segment_utils import(
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restore_result,
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)
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from diffusers import (
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StableDiffusionInstructPix2PixPipeline,
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EulerAncestralDiscreteScheduler,
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)
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from controlnet_aux import (
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@@ -30,10 +32,18 @@ DEFAULT_NEGATIVE_PROMPT = "worst quality, normal quality, low quality, low res,
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DEFAULT_CATEGORY = "hair"
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-
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BASE_MODEL,
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torch_dtype=torch.float16,
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use_safetensors=True,
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)
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basepipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(basepipeline.scheduler.config)
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@@ -51,10 +61,15 @@ def image_to_image(
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guidance_scale: float,
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image_guidance_scale: float,
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generate_size: int,
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):
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run_task_time = 0
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time_cost_str = ''
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run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
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generator = torch.Generator(device=DEVICE).manual_seed(seed)
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generated_image = basepipeline(
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@@ -65,6 +80,8 @@ def image_to_image(
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guidance_scale=guidance_scale,
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image_guidance_scale=image_guidance_scale,
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num_inference_steps=num_steps,
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).images[0]
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run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
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@@ -110,7 +127,6 @@ def create_demo() -> gr.Blocks:
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seed = gr.Number(label="Seed", value=8)
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category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
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cond_scale1 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="Cond_scale1")
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-
cond_scale2 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="Cond_scale2")
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g_btn = gr.Button("Edit Image")
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with gr.Row():
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@@ -129,7 +145,7 @@ def create_demo() -> gr.Blocks:
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outputs=[origin_area_image, croper],
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).success(
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fn=image_to_image,
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-
inputs=[origin_area_image, edit_prompt,seed, num_steps, guidance_scale, image_guidance_scale, generate_size],
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outputs=[generated_image, generated_cost],
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).success(
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fn=restore_result,
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restore_result,
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)
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from diffusers import (
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DiffusionPipeline,
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StableDiffusionInstructPix2PixPipeline,
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EulerAncestralDiscreteScheduler,
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+
T2IAdapter,
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)
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from controlnet_aux import (
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DEFAULT_CATEGORY = "hair"
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adapter = T2IAdapter.from_pretrained(
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"TencentARC/t2iadapter_canny_sd15v2",
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torch_dtype=torch.float16,
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varient="fp16",
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)
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+
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+
basepipeline = DiffusionPipeline.from_pretrained(
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BASE_MODEL,
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torch_dtype=torch.float16,
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use_safetensors=True,
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adapter=adapter,
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custom_pipeline="./pipelines/pipeline_sd_adapter_p2p.py",
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)
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basepipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(basepipeline.scheduler.config)
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guidance_scale: float,
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image_guidance_scale: float,
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generate_size: int,
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+
cond_scale1: float = 1.2,
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):
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run_task_time = 0
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time_cost_str = ''
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run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
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+
canny_image = CannyDetector()(input_image, 384, generate_size)
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+
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+
cond_image = canny_image
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+
cond_scale = cond_scale1
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generator = torch.Generator(device=DEVICE).manual_seed(seed)
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generated_image = basepipeline(
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guidance_scale=guidance_scale,
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image_guidance_scale=image_guidance_scale,
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num_inference_steps=num_steps,
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+
adapter_image=cond_image,
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adapter_conditioning_scale=cond_scale,
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).images[0]
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run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
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seed = gr.Number(label="Seed", value=8)
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category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
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cond_scale1 = gr.Slider(minimum=0, maximum=3, value=1.2, step=0.1, label="Cond_scale1")
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g_btn = gr.Button("Edit Image")
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with gr.Row():
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outputs=[origin_area_image, croper],
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).success(
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fn=image_to_image,
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+
inputs=[origin_area_image, edit_prompt,seed, num_steps, guidance_scale, image_guidance_scale, generate_size, cond_scale1],
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outputs=[generated_image, generated_cost],
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).success(
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fn=restore_result,
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pipelines/pipeline_sd_adapter_p2p.py
ADDED
@@ -0,0 +1,1034 @@
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1 |
+
# Copyright 2024 The InstructPix2Pix Authors and 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 |
+
import inspect
|
16 |
+
from typing import Any, Callable, Dict, List, Optional, Union
|
17 |
+
|
18 |
+
import numpy as np
|
19 |
+
import PIL.Image
|
20 |
+
import torch
|
21 |
+
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
|
22 |
+
|
23 |
+
from diffusers.callbacks import (
|
24 |
+
MultiPipelineCallbacks,
|
25 |
+
PipelineCallback,
|
26 |
+
)
|
27 |
+
|
28 |
+
from diffusers.image_processor import (
|
29 |
+
PipelineImageInput,
|
30 |
+
VaeImageProcessor,
|
31 |
+
)
|
32 |
+
|
33 |
+
from diffusers.loaders import (
|
34 |
+
IPAdapterMixin,
|
35 |
+
StableDiffusionLoraLoaderMixin,
|
36 |
+
TextualInversionLoaderMixin,
|
37 |
+
)
|
38 |
+
|
39 |
+
from diffusers.models import (
|
40 |
+
AutoencoderKL,
|
41 |
+
ImageProjection,
|
42 |
+
MultiAdapter,
|
43 |
+
T2IAdapter,
|
44 |
+
UNet2DConditionModel,
|
45 |
+
)
|
46 |
+
|
47 |
+
from diffusers.schedulers import (
|
48 |
+
KarrasDiffusionSchedulers,
|
49 |
+
)
|
50 |
+
|
51 |
+
from diffusers.utils import (
|
52 |
+
PIL_INTERPOLATION,
|
53 |
+
deprecate,
|
54 |
+
logging,
|
55 |
+
)
|
56 |
+
|
57 |
+
from diffusers.utils.torch_utils import (
|
58 |
+
randn_tensor,
|
59 |
+
)
|
60 |
+
|
61 |
+
from diffusers.pipelines.pipeline_utils import (
|
62 |
+
DiffusionPipeline,
|
63 |
+
StableDiffusionMixin,
|
64 |
+
)
|
65 |
+
|
66 |
+
from diffusers.pipelines.stable_diffusion.pipeline_output import (
|
67 |
+
StableDiffusionPipelineOutput,
|
68 |
+
)
|
69 |
+
|
70 |
+
from diffusers.pipelines.stable_diffusion.safety_checker import (
|
71 |
+
StableDiffusionSafetyChecker,
|
72 |
+
)
|
73 |
+
|
74 |
+
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
|
75 |
+
|
76 |
+
def _preprocess_adapter_image(image, height, width):
|
77 |
+
if isinstance(image, torch.Tensor):
|
78 |
+
return image
|
79 |
+
elif isinstance(image, PIL.Image.Image):
|
80 |
+
image = [image]
|
81 |
+
|
82 |
+
if isinstance(image[0], PIL.Image.Image):
|
83 |
+
image = [np.array(i.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])) for i in image]
|
84 |
+
image = [
|
85 |
+
i[None, ..., None] if i.ndim == 2 else i[None, ...] for i in image
|
86 |
+
] # expand [h, w] or [h, w, c] to [b, h, w, c]
|
87 |
+
image = np.concatenate(image, axis=0)
|
88 |
+
image = np.array(image).astype(np.float32) / 255.0
|
89 |
+
image = image.transpose(0, 3, 1, 2)
|
90 |
+
image = torch.from_numpy(image)
|
91 |
+
elif isinstance(image[0], torch.Tensor):
|
92 |
+
if image[0].ndim == 3:
|
93 |
+
image = torch.stack(image, dim=0)
|
94 |
+
elif image[0].ndim == 4:
|
95 |
+
image = torch.cat(image, dim=0)
|
96 |
+
else:
|
97 |
+
raise ValueError(
|
98 |
+
f"Invalid image tensor! Expecting image tensor with 3 or 4 dimension, but recive: {image[0].ndim}"
|
99 |
+
)
|
100 |
+
return image
|
101 |
+
|
102 |
+
|
103 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.preprocess
|
104 |
+
def preprocess(image):
|
105 |
+
deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
|
106 |
+
deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
|
107 |
+
if isinstance(image, torch.Tensor):
|
108 |
+
return image
|
109 |
+
elif isinstance(image, PIL.Image.Image):
|
110 |
+
image = [image]
|
111 |
+
|
112 |
+
if isinstance(image[0], PIL.Image.Image):
|
113 |
+
w, h = image[0].size
|
114 |
+
w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
|
115 |
+
|
116 |
+
image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
|
117 |
+
image = np.concatenate(image, axis=0)
|
118 |
+
image = np.array(image).astype(np.float32) / 255.0
|
119 |
+
image = image.transpose(0, 3, 1, 2)
|
120 |
+
image = 2.0 * image - 1.0
|
121 |
+
image = torch.from_numpy(image)
|
122 |
+
elif isinstance(image[0], torch.Tensor):
|
123 |
+
image = torch.cat(image, dim=0)
|
124 |
+
return image
|
125 |
+
|
126 |
+
|
127 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
|
128 |
+
def retrieve_latents(
|
129 |
+
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
|
130 |
+
):
|
131 |
+
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
|
132 |
+
return encoder_output.latent_dist.sample(generator)
|
133 |
+
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
|
134 |
+
return encoder_output.latent_dist.mode()
|
135 |
+
elif hasattr(encoder_output, "latents"):
|
136 |
+
return encoder_output.latents
|
137 |
+
else:
|
138 |
+
raise AttributeError("Could not access latents of provided encoder_output")
|
139 |
+
|
140 |
+
|
141 |
+
class StableDiffusionInstructPix2PixPipeline(
|
142 |
+
DiffusionPipeline,
|
143 |
+
StableDiffusionMixin,
|
144 |
+
TextualInversionLoaderMixin,
|
145 |
+
StableDiffusionLoraLoaderMixin,
|
146 |
+
IPAdapterMixin,
|
147 |
+
):
|
148 |
+
r"""
|
149 |
+
Pipeline for pixel-level image editing by following text instructions (based on Stable Diffusion).
|
150 |
+
|
151 |
+
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
152 |
+
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
153 |
+
|
154 |
+
The pipeline also inherits the following loading methods:
|
155 |
+
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
|
156 |
+
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
|
157 |
+
- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
|
158 |
+
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
|
159 |
+
|
160 |
+
Args:
|
161 |
+
vae ([`AutoencoderKL`]):
|
162 |
+
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
|
163 |
+
text_encoder ([`~transformers.CLIPTextModel`]):
|
164 |
+
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
|
165 |
+
tokenizer ([`~transformers.CLIPTokenizer`]):
|
166 |
+
A `CLIPTokenizer` to tokenize text.
|
167 |
+
unet ([`UNet2DConditionModel`]):
|
168 |
+
A `UNet2DConditionModel` to denoise the encoded image latents.
|
169 |
+
scheduler ([`SchedulerMixin`]):
|
170 |
+
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
171 |
+
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
172 |
+
safety_checker ([`StableDiffusionSafetyChecker`]):
|
173 |
+
Classification module that estimates whether generated images could be considered offensive or harmful.
|
174 |
+
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
|
175 |
+
about a model's potential harms.
|
176 |
+
feature_extractor ([`~transformers.CLIPImageProcessor`]):
|
177 |
+
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
|
178 |
+
"""
|
179 |
+
|
180 |
+
model_cpu_offload_seq = "text_encoder->unet->vae"
|
181 |
+
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
|
182 |
+
_exclude_from_cpu_offload = ["safety_checker"]
|
183 |
+
_callback_tensor_inputs = ["latents", "prompt_embeds", "image_latents"]
|
184 |
+
|
185 |
+
def __init__(
|
186 |
+
self,
|
187 |
+
vae: AutoencoderKL,
|
188 |
+
text_encoder: CLIPTextModel,
|
189 |
+
tokenizer: CLIPTokenizer,
|
190 |
+
unet: UNet2DConditionModel,
|
191 |
+
adapter: Union[T2IAdapter, MultiAdapter, List[T2IAdapter]],
|
192 |
+
scheduler: KarrasDiffusionSchedulers,
|
193 |
+
safety_checker: StableDiffusionSafetyChecker,
|
194 |
+
feature_extractor: CLIPImageProcessor,
|
195 |
+
image_encoder: Optional[CLIPVisionModelWithProjection] = None,
|
196 |
+
requires_safety_checker: bool = True,
|
197 |
+
):
|
198 |
+
super().__init__()
|
199 |
+
|
200 |
+
if safety_checker is None and requires_safety_checker:
|
201 |
+
logger.warning(
|
202 |
+
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
|
203 |
+
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
|
204 |
+
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
|
205 |
+
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
|
206 |
+
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
|
207 |
+
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
|
208 |
+
)
|
209 |
+
|
210 |
+
if safety_checker is not None and feature_extractor is None:
|
211 |
+
raise ValueError(
|
212 |
+
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
|
213 |
+
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
|
214 |
+
)
|
215 |
+
|
216 |
+
self.register_modules(
|
217 |
+
vae=vae,
|
218 |
+
text_encoder=text_encoder,
|
219 |
+
tokenizer=tokenizer,
|
220 |
+
unet=unet,
|
221 |
+
adapter=adapter,
|
222 |
+
scheduler=scheduler,
|
223 |
+
safety_checker=safety_checker,
|
224 |
+
feature_extractor=feature_extractor,
|
225 |
+
image_encoder=image_encoder,
|
226 |
+
)
|
227 |
+
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
228 |
+
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
229 |
+
self.register_to_config(requires_safety_checker=requires_safety_checker)
|
230 |
+
|
231 |
+
@torch.no_grad()
|
232 |
+
def __call__(
|
233 |
+
self,
|
234 |
+
prompt: Union[str, List[str]] = None,
|
235 |
+
image: PipelineImageInput = None,
|
236 |
+
height: Optional[int] = None,
|
237 |
+
width: Optional[int] = None,
|
238 |
+
adapter_image: PipelineImageInput = None,
|
239 |
+
num_inference_steps: int = 100,
|
240 |
+
guidance_scale: float = 7.5,
|
241 |
+
image_guidance_scale: float = 1.5,
|
242 |
+
negative_prompt: Optional[Union[str, List[str]]] = None,
|
243 |
+
num_images_per_prompt: Optional[int] = 1,
|
244 |
+
eta: float = 0.0,
|
245 |
+
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
246 |
+
latents: Optional[torch.Tensor] = None,
|
247 |
+
prompt_embeds: Optional[torch.Tensor] = None,
|
248 |
+
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
249 |
+
ip_adapter_image: Optional[PipelineImageInput] = None,
|
250 |
+
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
|
251 |
+
output_type: Optional[str] = "pil",
|
252 |
+
return_dict: bool = True,
|
253 |
+
adapter_conditioning_scale: Union[float, List[float]] = 1.0,
|
254 |
+
callback_on_step_end: Optional[
|
255 |
+
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
|
256 |
+
] = None,
|
257 |
+
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
258 |
+
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
259 |
+
**kwargs,
|
260 |
+
):
|
261 |
+
r"""
|
262 |
+
The call function to the pipeline for generation.
|
263 |
+
|
264 |
+
Args:
|
265 |
+
prompt (`str` or `List[str]`, *optional*):
|
266 |
+
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
267 |
+
image (`torch.Tensor` `np.ndarray`, `PIL.Image.Image`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
|
268 |
+
`Image` or tensor representing an image batch to be repainted according to `prompt`. Can also accept
|
269 |
+
image latents as `image`, but if passing latents directly it is not encoded again.
|
270 |
+
num_inference_steps (`int`, *optional*, defaults to 100):
|
271 |
+
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
272 |
+
expense of slower inference.
|
273 |
+
guidance_scale (`float`, *optional*, defaults to 7.5):
|
274 |
+
A higher guidance scale value encourages the model to generate images closely linked to the text
|
275 |
+
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
276 |
+
image_guidance_scale (`float`, *optional*, defaults to 1.5):
|
277 |
+
Push the generated image towards the initial `image`. Image guidance scale is enabled by setting
|
278 |
+
`image_guidance_scale > 1`. Higher image guidance scale encourages generated images that are closely
|
279 |
+
linked to the source `image`, usually at the expense of lower image quality. This pipeline requires a
|
280 |
+
value of at least `1`.
|
281 |
+
negative_prompt (`str` or `List[str]`, *optional*):
|
282 |
+
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
|
283 |
+
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
|
284 |
+
num_images_per_prompt (`int`, *optional*, defaults to 1):
|
285 |
+
The number of images to generate per prompt.
|
286 |
+
eta (`float`, *optional*, defaults to 0.0):
|
287 |
+
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
|
288 |
+
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
|
289 |
+
generator (`torch.Generator`, *optional*):
|
290 |
+
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
291 |
+
generation deterministic.
|
292 |
+
latents (`torch.Tensor`, *optional*):
|
293 |
+
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
294 |
+
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
295 |
+
tensor is generated by sampling using the supplied random `generator`.
|
296 |
+
prompt_embeds (`torch.Tensor`, *optional*):
|
297 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
|
298 |
+
provided, text embeddings are generated from the `prompt` input argument.
|
299 |
+
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
300 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
|
301 |
+
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
|
302 |
+
ip_adapter_image: (`PipelineImageInput`, *optional*):
|
303 |
+
Optional image input to work with IP Adapters.
|
304 |
+
output_type (`str`, *optional*, defaults to `"pil"`):
|
305 |
+
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
306 |
+
return_dict (`bool`, *optional*, defaults to `True`):
|
307 |
+
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
308 |
+
plain tuple.
|
309 |
+
callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
|
310 |
+
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
|
311 |
+
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
|
312 |
+
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
|
313 |
+
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
|
314 |
+
callback_on_step_end_tensor_inputs (`List`, *optional*):
|
315 |
+
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
|
316 |
+
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
|
317 |
+
`._callback_tensor_inputs` attribute of your pipeline class.
|
318 |
+
cross_attention_kwargs (`dict`, *optional*):
|
319 |
+
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
|
320 |
+
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
|
321 |
+
|
322 |
+
Examples:
|
323 |
+
|
324 |
+
```py
|
325 |
+
>>> import PIL
|
326 |
+
>>> import requests
|
327 |
+
>>> import torch
|
328 |
+
>>> from io import BytesIO
|
329 |
+
|
330 |
+
>>> from diffusers import StableDiffusionInstructPix2PixPipeline
|
331 |
+
|
332 |
+
|
333 |
+
>>> def download_image(url):
|
334 |
+
... response = requests.get(url)
|
335 |
+
... return PIL.Image.open(BytesIO(response.content)).convert("RGB")
|
336 |
+
|
337 |
+
|
338 |
+
>>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"
|
339 |
+
|
340 |
+
>>> image = download_image(img_url).resize((512, 512))
|
341 |
+
|
342 |
+
>>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
343 |
+
... "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
|
344 |
+
... )
|
345 |
+
>>> pipe = pipe.to("cuda")
|
346 |
+
|
347 |
+
>>> prompt = "make the mountains snowy"
|
348 |
+
>>> image = pipe(prompt=prompt, image=image).images[0]
|
349 |
+
```
|
350 |
+
|
351 |
+
Returns:
|
352 |
+
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
353 |
+
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
|
354 |
+
otherwise a `tuple` is returned where the first element is a list with the generated images and the
|
355 |
+
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
356 |
+
"not-safe-for-work" (nsfw) content.
|
357 |
+
"""
|
358 |
+
height, width = self._default_height_width(height, width, adapter_image)
|
359 |
+
device = self._execution_device
|
360 |
+
|
361 |
+
if isinstance(self.adapter, MultiAdapter):
|
362 |
+
adapter_input = []
|
363 |
+
|
364 |
+
for one_image in adapter_image:
|
365 |
+
one_image = _preprocess_adapter_image(one_image, height, width)
|
366 |
+
one_image = one_image.to(device=device, dtype=self.adapter.dtype)
|
367 |
+
adapter_input.append(one_image)
|
368 |
+
else:
|
369 |
+
adapter_input = _preprocess_adapter_image(adapter_image, height, width)
|
370 |
+
adapter_input = adapter_input.to(device=device, dtype=self.adapter.dtype)
|
371 |
+
|
372 |
+
callback = kwargs.pop("callback", None)
|
373 |
+
callback_steps = kwargs.pop("callback_steps", None)
|
374 |
+
|
375 |
+
if callback is not None:
|
376 |
+
deprecate(
|
377 |
+
"callback",
|
378 |
+
"1.0.0",
|
379 |
+
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
380 |
+
)
|
381 |
+
if callback_steps is not None:
|
382 |
+
deprecate(
|
383 |
+
"callback_steps",
|
384 |
+
"1.0.0",
|
385 |
+
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
|
386 |
+
)
|
387 |
+
|
388 |
+
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
|
389 |
+
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
|
390 |
+
|
391 |
+
# 0. Check inputs
|
392 |
+
self.check_inputs(
|
393 |
+
prompt,
|
394 |
+
callback_steps,
|
395 |
+
negative_prompt,
|
396 |
+
prompt_embeds,
|
397 |
+
negative_prompt_embeds,
|
398 |
+
ip_adapter_image,
|
399 |
+
ip_adapter_image_embeds,
|
400 |
+
callback_on_step_end_tensor_inputs,
|
401 |
+
)
|
402 |
+
self._guidance_scale = guidance_scale
|
403 |
+
self._image_guidance_scale = image_guidance_scale
|
404 |
+
|
405 |
+
device = self._execution_device
|
406 |
+
|
407 |
+
if image is None:
|
408 |
+
raise ValueError("`image` input cannot be undefined.")
|
409 |
+
|
410 |
+
# 1. Define call parameters
|
411 |
+
if prompt is not None and isinstance(prompt, str):
|
412 |
+
batch_size = 1
|
413 |
+
elif prompt is not None and isinstance(prompt, list):
|
414 |
+
batch_size = len(prompt)
|
415 |
+
else:
|
416 |
+
batch_size = prompt_embeds.shape[0]
|
417 |
+
|
418 |
+
device = self._execution_device
|
419 |
+
|
420 |
+
# 2. Encode input prompt
|
421 |
+
prompt_embeds = self._encode_prompt(
|
422 |
+
prompt,
|
423 |
+
device,
|
424 |
+
num_images_per_prompt,
|
425 |
+
self.do_classifier_free_guidance,
|
426 |
+
negative_prompt,
|
427 |
+
prompt_embeds=prompt_embeds,
|
428 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
429 |
+
)
|
430 |
+
|
431 |
+
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
|
432 |
+
image_embeds = self.prepare_ip_adapter_image_embeds(
|
433 |
+
ip_adapter_image,
|
434 |
+
ip_adapter_image_embeds,
|
435 |
+
device,
|
436 |
+
batch_size * num_images_per_prompt,
|
437 |
+
self.do_classifier_free_guidance,
|
438 |
+
)
|
439 |
+
# 3. Preprocess image
|
440 |
+
image = self.image_processor.preprocess(image)
|
441 |
+
|
442 |
+
# 4. set timesteps
|
443 |
+
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
444 |
+
timesteps = self.scheduler.timesteps
|
445 |
+
|
446 |
+
# 5. Prepare Image latents
|
447 |
+
image_latents = self.prepare_image_latents(
|
448 |
+
image,
|
449 |
+
batch_size,
|
450 |
+
num_images_per_prompt,
|
451 |
+
prompt_embeds.dtype,
|
452 |
+
device,
|
453 |
+
self.do_classifier_free_guidance,
|
454 |
+
)
|
455 |
+
|
456 |
+
height, width = image_latents.shape[-2:]
|
457 |
+
height = height * self.vae_scale_factor
|
458 |
+
width = width * self.vae_scale_factor
|
459 |
+
|
460 |
+
# 6. Prepare latent variables
|
461 |
+
num_channels_latents = self.vae.config.latent_channels
|
462 |
+
latents = self.prepare_latents(
|
463 |
+
batch_size * num_images_per_prompt,
|
464 |
+
num_channels_latents,
|
465 |
+
height,
|
466 |
+
width,
|
467 |
+
prompt_embeds.dtype,
|
468 |
+
device,
|
469 |
+
generator,
|
470 |
+
latents,
|
471 |
+
)
|
472 |
+
|
473 |
+
# 7. Check that shapes of latents and image match the UNet channels
|
474 |
+
num_channels_image = image_latents.shape[1]
|
475 |
+
if num_channels_latents + num_channels_image != self.unet.config.in_channels:
|
476 |
+
raise ValueError(
|
477 |
+
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
478 |
+
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
479 |
+
f" `num_channels_image`: {num_channels_image} "
|
480 |
+
f" = {num_channels_latents+num_channels_image}. Please verify the config of"
|
481 |
+
" `pipeline.unet` or your `image` input."
|
482 |
+
)
|
483 |
+
|
484 |
+
# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
|
485 |
+
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
|
486 |
+
|
487 |
+
# 8.1 Add image embeds for IP-Adapter
|
488 |
+
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
|
489 |
+
|
490 |
+
# 9. Denoising loop
|
491 |
+
if isinstance(self.adapter, MultiAdapter):
|
492 |
+
adapter_state = self.adapter(adapter_input, adapter_conditioning_scale)
|
493 |
+
for k, v in enumerate(adapter_state):
|
494 |
+
adapter_state[k] = v
|
495 |
+
else:
|
496 |
+
adapter_state = self.adapter(adapter_input)
|
497 |
+
for k, v in enumerate(adapter_state):
|
498 |
+
adapter_state[k] = v * adapter_conditioning_scale
|
499 |
+
if num_images_per_prompt > 1:
|
500 |
+
for k, v in enumerate(adapter_state):
|
501 |
+
adapter_state[k] = v.repeat(num_images_per_prompt, 1, 1, 1)
|
502 |
+
if self.do_classifier_free_guidance:
|
503 |
+
for k, v in enumerate(adapter_state):
|
504 |
+
adapter_state[k] = torch.cat([v] * 2, dim=0)
|
505 |
+
|
506 |
+
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
507 |
+
self._num_timesteps = len(timesteps)
|
508 |
+
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
509 |
+
for i, t in enumerate(timesteps):
|
510 |
+
# Expand the latents if we are doing classifier free guidance.
|
511 |
+
# The latents are expanded 3 times because for pix2pix the guidance\
|
512 |
+
# is applied for both the text and the input image.
|
513 |
+
latent_model_input = torch.cat([latents] * 3) if self.do_classifier_free_guidance else latents
|
514 |
+
|
515 |
+
# concat latents, image_latents in the channel dimension
|
516 |
+
scaled_latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
517 |
+
scaled_latent_model_input = torch.cat([scaled_latent_model_input, image_latents], dim=1)
|
518 |
+
|
519 |
+
# predict the noise residual
|
520 |
+
noise_pred = self.unet(
|
521 |
+
scaled_latent_model_input,
|
522 |
+
t,
|
523 |
+
encoder_hidden_states=prompt_embeds,
|
524 |
+
added_cond_kwargs=added_cond_kwargs,
|
525 |
+
down_intrablock_additional_residuals=[state.clone() for state in adapter_state],
|
526 |
+
cross_attention_kwargs=cross_attention_kwargs,
|
527 |
+
return_dict=False,
|
528 |
+
)[0]
|
529 |
+
|
530 |
+
# perform guidance
|
531 |
+
if self.do_classifier_free_guidance:
|
532 |
+
noise_pred_text, noise_pred_image, noise_pred_uncond = noise_pred.chunk(3)
|
533 |
+
noise_pred = (
|
534 |
+
noise_pred_uncond
|
535 |
+
+ self.guidance_scale * (noise_pred_text - noise_pred_image)
|
536 |
+
+ self.image_guidance_scale * (noise_pred_image - noise_pred_uncond)
|
537 |
+
)
|
538 |
+
|
539 |
+
# compute the previous noisy sample x_t -> x_t-1
|
540 |
+
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
|
541 |
+
|
542 |
+
if callback_on_step_end is not None:
|
543 |
+
callback_kwargs = {}
|
544 |
+
for k in callback_on_step_end_tensor_inputs:
|
545 |
+
callback_kwargs[k] = locals()[k]
|
546 |
+
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
|
547 |
+
|
548 |
+
latents = callback_outputs.pop("latents", latents)
|
549 |
+
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
|
550 |
+
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
|
551 |
+
image_latents = callback_outputs.pop("image_latents", image_latents)
|
552 |
+
|
553 |
+
# call the callback, if provided
|
554 |
+
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
|
555 |
+
progress_bar.update()
|
556 |
+
if callback is not None and i % callback_steps == 0:
|
557 |
+
step_idx = i // getattr(self.scheduler, "order", 1)
|
558 |
+
callback(step_idx, t, latents)
|
559 |
+
|
560 |
+
if not output_type == "latent":
|
561 |
+
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
|
562 |
+
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
|
563 |
+
else:
|
564 |
+
image = latents
|
565 |
+
has_nsfw_concept = None
|
566 |
+
|
567 |
+
if has_nsfw_concept is None:
|
568 |
+
do_denormalize = [True] * image.shape[0]
|
569 |
+
else:
|
570 |
+
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
571 |
+
|
572 |
+
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
|
573 |
+
|
574 |
+
# Offload all models
|
575 |
+
self.maybe_free_model_hooks()
|
576 |
+
|
577 |
+
if not return_dict:
|
578 |
+
return (image, has_nsfw_concept)
|
579 |
+
|
580 |
+
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
|
581 |
+
|
582 |
+
def _encode_prompt(
|
583 |
+
self,
|
584 |
+
prompt,
|
585 |
+
device,
|
586 |
+
num_images_per_prompt,
|
587 |
+
do_classifier_free_guidance,
|
588 |
+
negative_prompt=None,
|
589 |
+
prompt_embeds: Optional[torch.Tensor] = None,
|
590 |
+
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
591 |
+
):
|
592 |
+
r"""
|
593 |
+
Encodes the prompt into text encoder hidden states.
|
594 |
+
|
595 |
+
Args:
|
596 |
+
prompt (`str` or `List[str]`, *optional*):
|
597 |
+
prompt to be encoded
|
598 |
+
device: (`torch.device`):
|
599 |
+
torch device
|
600 |
+
num_images_per_prompt (`int`):
|
601 |
+
number of images that should be generated per prompt
|
602 |
+
do_classifier_free_guidance (`bool`):
|
603 |
+
whether to use classifier free guidance or not
|
604 |
+
negative_ prompt (`str` or `List[str]`, *optional*):
|
605 |
+
The prompt or prompts not to guide the image generation. If not defined, one has to pass
|
606 |
+
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
|
607 |
+
less than `1`).
|
608 |
+
prompt_embeds (`torch.Tensor`, *optional*):
|
609 |
+
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
610 |
+
provided, text embeddings will be generated from `prompt` input argument.
|
611 |
+
negative_prompt_embeds (`torch.Tensor`, *optional*):
|
612 |
+
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
613 |
+
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
614 |
+
argument.
|
615 |
+
"""
|
616 |
+
if prompt is not None and isinstance(prompt, str):
|
617 |
+
batch_size = 1
|
618 |
+
elif prompt is not None and isinstance(prompt, list):
|
619 |
+
batch_size = len(prompt)
|
620 |
+
else:
|
621 |
+
batch_size = prompt_embeds.shape[0]
|
622 |
+
|
623 |
+
if prompt_embeds is None:
|
624 |
+
# textual inversion: process multi-vector tokens if necessary
|
625 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
626 |
+
prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
|
627 |
+
|
628 |
+
text_inputs = self.tokenizer(
|
629 |
+
prompt,
|
630 |
+
padding="max_length",
|
631 |
+
max_length=self.tokenizer.model_max_length,
|
632 |
+
truncation=True,
|
633 |
+
return_tensors="pt",
|
634 |
+
)
|
635 |
+
text_input_ids = text_inputs.input_ids
|
636 |
+
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
|
637 |
+
|
638 |
+
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
|
639 |
+
text_input_ids, untruncated_ids
|
640 |
+
):
|
641 |
+
removed_text = self.tokenizer.batch_decode(
|
642 |
+
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
|
643 |
+
)
|
644 |
+
logger.warning(
|
645 |
+
"The following part of your input was truncated because CLIP can only handle sequences up to"
|
646 |
+
f" {self.tokenizer.model_max_length} tokens: {removed_text}"
|
647 |
+
)
|
648 |
+
|
649 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
650 |
+
attention_mask = text_inputs.attention_mask.to(device)
|
651 |
+
else:
|
652 |
+
attention_mask = None
|
653 |
+
|
654 |
+
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
|
655 |
+
prompt_embeds = prompt_embeds[0]
|
656 |
+
|
657 |
+
if self.text_encoder is not None:
|
658 |
+
prompt_embeds_dtype = self.text_encoder.dtype
|
659 |
+
else:
|
660 |
+
prompt_embeds_dtype = self.unet.dtype
|
661 |
+
|
662 |
+
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
663 |
+
|
664 |
+
bs_embed, seq_len, _ = prompt_embeds.shape
|
665 |
+
# duplicate text embeddings for each generation per prompt, using mps friendly method
|
666 |
+
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
667 |
+
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
|
668 |
+
|
669 |
+
# get unconditional embeddings for classifier free guidance
|
670 |
+
if do_classifier_free_guidance and negative_prompt_embeds is None:
|
671 |
+
uncond_tokens: List[str]
|
672 |
+
if negative_prompt is None:
|
673 |
+
uncond_tokens = [""] * batch_size
|
674 |
+
elif type(prompt) is not type(negative_prompt):
|
675 |
+
raise TypeError(
|
676 |
+
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
|
677 |
+
f" {type(prompt)}."
|
678 |
+
)
|
679 |
+
elif isinstance(negative_prompt, str):
|
680 |
+
uncond_tokens = [negative_prompt]
|
681 |
+
elif batch_size != len(negative_prompt):
|
682 |
+
raise ValueError(
|
683 |
+
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
|
684 |
+
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
|
685 |
+
" the batch size of `prompt`."
|
686 |
+
)
|
687 |
+
else:
|
688 |
+
uncond_tokens = negative_prompt
|
689 |
+
|
690 |
+
# textual inversion: process multi-vector tokens if necessary
|
691 |
+
if isinstance(self, TextualInversionLoaderMixin):
|
692 |
+
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
|
693 |
+
|
694 |
+
max_length = prompt_embeds.shape[1]
|
695 |
+
uncond_input = self.tokenizer(
|
696 |
+
uncond_tokens,
|
697 |
+
padding="max_length",
|
698 |
+
max_length=max_length,
|
699 |
+
truncation=True,
|
700 |
+
return_tensors="pt",
|
701 |
+
)
|
702 |
+
|
703 |
+
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
|
704 |
+
attention_mask = uncond_input.attention_mask.to(device)
|
705 |
+
else:
|
706 |
+
attention_mask = None
|
707 |
+
|
708 |
+
negative_prompt_embeds = self.text_encoder(
|
709 |
+
uncond_input.input_ids.to(device),
|
710 |
+
attention_mask=attention_mask,
|
711 |
+
)
|
712 |
+
negative_prompt_embeds = negative_prompt_embeds[0]
|
713 |
+
|
714 |
+
if do_classifier_free_guidance:
|
715 |
+
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
716 |
+
seq_len = negative_prompt_embeds.shape[1]
|
717 |
+
|
718 |
+
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
|
719 |
+
|
720 |
+
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
|
721 |
+
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
|
722 |
+
|
723 |
+
# For classifier free guidance, we need to do two forward passes.
|
724 |
+
# Here we concatenate the unconditional and text embeddings into a single batch
|
725 |
+
# to avoid doing two forward passes
|
726 |
+
# pix2pix has two negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]
|
727 |
+
prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds, negative_prompt_embeds])
|
728 |
+
|
729 |
+
return prompt_embeds
|
730 |
+
|
731 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
|
732 |
+
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
|
733 |
+
dtype = next(self.image_encoder.parameters()).dtype
|
734 |
+
|
735 |
+
if not isinstance(image, torch.Tensor):
|
736 |
+
image = self.feature_extractor(image, return_tensors="pt").pixel_values
|
737 |
+
|
738 |
+
image = image.to(device=device, dtype=dtype)
|
739 |
+
if output_hidden_states:
|
740 |
+
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
|
741 |
+
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
|
742 |
+
uncond_image_enc_hidden_states = self.image_encoder(
|
743 |
+
torch.zeros_like(image), output_hidden_states=True
|
744 |
+
).hidden_states[-2]
|
745 |
+
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
|
746 |
+
num_images_per_prompt, dim=0
|
747 |
+
)
|
748 |
+
return image_enc_hidden_states, uncond_image_enc_hidden_states
|
749 |
+
else:
|
750 |
+
image_embeds = self.image_encoder(image).image_embeds
|
751 |
+
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
|
752 |
+
uncond_image_embeds = torch.zeros_like(image_embeds)
|
753 |
+
|
754 |
+
return image_embeds, uncond_image_embeds
|
755 |
+
|
756 |
+
def prepare_ip_adapter_image_embeds(
|
757 |
+
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
|
758 |
+
):
|
759 |
+
if ip_adapter_image_embeds is None:
|
760 |
+
if not isinstance(ip_adapter_image, list):
|
761 |
+
ip_adapter_image = [ip_adapter_image]
|
762 |
+
|
763 |
+
if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
|
764 |
+
raise ValueError(
|
765 |
+
f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
|
766 |
+
)
|
767 |
+
|
768 |
+
image_embeds = []
|
769 |
+
for single_ip_adapter_image, image_proj_layer in zip(
|
770 |
+
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
|
771 |
+
):
|
772 |
+
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
|
773 |
+
single_image_embeds, single_negative_image_embeds = self.encode_image(
|
774 |
+
single_ip_adapter_image, device, 1, output_hidden_state
|
775 |
+
)
|
776 |
+
single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
|
777 |
+
single_negative_image_embeds = torch.stack(
|
778 |
+
[single_negative_image_embeds] * num_images_per_prompt, dim=0
|
779 |
+
)
|
780 |
+
|
781 |
+
if do_classifier_free_guidance:
|
782 |
+
single_image_embeds = torch.cat(
|
783 |
+
[single_image_embeds, single_negative_image_embeds, single_negative_image_embeds]
|
784 |
+
)
|
785 |
+
single_image_embeds = single_image_embeds.to(device)
|
786 |
+
|
787 |
+
image_embeds.append(single_image_embeds)
|
788 |
+
else:
|
789 |
+
repeat_dims = [1]
|
790 |
+
image_embeds = []
|
791 |
+
for single_image_embeds in ip_adapter_image_embeds:
|
792 |
+
if do_classifier_free_guidance:
|
793 |
+
(
|
794 |
+
single_image_embeds,
|
795 |
+
single_negative_image_embeds,
|
796 |
+
single_negative_image_embeds,
|
797 |
+
) = single_image_embeds.chunk(3)
|
798 |
+
single_image_embeds = single_image_embeds.repeat(
|
799 |
+
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
|
800 |
+
)
|
801 |
+
single_negative_image_embeds = single_negative_image_embeds.repeat(
|
802 |
+
num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
|
803 |
+
)
|
804 |
+
single_image_embeds = torch.cat(
|
805 |
+
[single_image_embeds, single_negative_image_embeds, single_negative_image_embeds]
|
806 |
+
)
|
807 |
+
else:
|
808 |
+
single_image_embeds = single_image_embeds.repeat(
|
809 |
+
num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
|
810 |
+
)
|
811 |
+
image_embeds.append(single_image_embeds)
|
812 |
+
|
813 |
+
return image_embeds
|
814 |
+
|
815 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
|
816 |
+
def run_safety_checker(self, image, device, dtype):
|
817 |
+
if self.safety_checker is None:
|
818 |
+
has_nsfw_concept = None
|
819 |
+
else:
|
820 |
+
if torch.is_tensor(image):
|
821 |
+
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
|
822 |
+
else:
|
823 |
+
feature_extractor_input = self.image_processor.numpy_to_pil(image)
|
824 |
+
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
|
825 |
+
image, has_nsfw_concept = self.safety_checker(
|
826 |
+
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
|
827 |
+
)
|
828 |
+
return image, has_nsfw_concept
|
829 |
+
|
830 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
831 |
+
def prepare_extra_step_kwargs(self, generator, eta):
|
832 |
+
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
833 |
+
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
834 |
+
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
|
835 |
+
# and should be between [0, 1]
|
836 |
+
|
837 |
+
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
838 |
+
extra_step_kwargs = {}
|
839 |
+
if accepts_eta:
|
840 |
+
extra_step_kwargs["eta"] = eta
|
841 |
+
|
842 |
+
# check if the scheduler accepts generator
|
843 |
+
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
844 |
+
if accepts_generator:
|
845 |
+
extra_step_kwargs["generator"] = generator
|
846 |
+
return extra_step_kwargs
|
847 |
+
|
848 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
|
849 |
+
def decode_latents(self, latents):
|
850 |
+
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
|
851 |
+
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
|
852 |
+
|
853 |
+
latents = 1 / self.vae.config.scaling_factor * latents
|
854 |
+
image = self.vae.decode(latents, return_dict=False)[0]
|
855 |
+
image = (image / 2 + 0.5).clamp(0, 1)
|
856 |
+
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
857 |
+
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
|
858 |
+
return image
|
859 |
+
|
860 |
+
def check_inputs(
|
861 |
+
self,
|
862 |
+
prompt,
|
863 |
+
callback_steps,
|
864 |
+
negative_prompt=None,
|
865 |
+
prompt_embeds=None,
|
866 |
+
negative_prompt_embeds=None,
|
867 |
+
ip_adapter_image=None,
|
868 |
+
ip_adapter_image_embeds=None,
|
869 |
+
callback_on_step_end_tensor_inputs=None,
|
870 |
+
):
|
871 |
+
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
|
872 |
+
raise ValueError(
|
873 |
+
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
|
874 |
+
f" {type(callback_steps)}."
|
875 |
+
)
|
876 |
+
|
877 |
+
if callback_on_step_end_tensor_inputs is not None and not all(
|
878 |
+
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
879 |
+
):
|
880 |
+
raise ValueError(
|
881 |
+
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
882 |
+
)
|
883 |
+
|
884 |
+
if prompt is not None and prompt_embeds is not None:
|
885 |
+
raise ValueError(
|
886 |
+
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
887 |
+
" only forward one of the two."
|
888 |
+
)
|
889 |
+
elif prompt is None and prompt_embeds is None:
|
890 |
+
raise ValueError(
|
891 |
+
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
892 |
+
)
|
893 |
+
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
|
894 |
+
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
895 |
+
|
896 |
+
if negative_prompt is not None and negative_prompt_embeds is not None:
|
897 |
+
raise ValueError(
|
898 |
+
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
899 |
+
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
900 |
+
)
|
901 |
+
|
902 |
+
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
903 |
+
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
904 |
+
raise ValueError(
|
905 |
+
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
906 |
+
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
907 |
+
f" {negative_prompt_embeds.shape}."
|
908 |
+
)
|
909 |
+
|
910 |
+
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
|
911 |
+
raise ValueError(
|
912 |
+
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
|
913 |
+
)
|
914 |
+
|
915 |
+
if ip_adapter_image_embeds is not None:
|
916 |
+
if not isinstance(ip_adapter_image_embeds, list):
|
917 |
+
raise ValueError(
|
918 |
+
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
|
919 |
+
)
|
920 |
+
elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
|
921 |
+
raise ValueError(
|
922 |
+
f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
|
923 |
+
)
|
924 |
+
|
925 |
+
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
|
926 |
+
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
|
927 |
+
shape = (
|
928 |
+
batch_size,
|
929 |
+
num_channels_latents,
|
930 |
+
int(height) // self.vae_scale_factor,
|
931 |
+
int(width) // self.vae_scale_factor,
|
932 |
+
)
|
933 |
+
if isinstance(generator, list) and len(generator) != batch_size:
|
934 |
+
raise ValueError(
|
935 |
+
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
936 |
+
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
937 |
+
)
|
938 |
+
|
939 |
+
if latents is None:
|
940 |
+
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
941 |
+
else:
|
942 |
+
latents = latents.to(device)
|
943 |
+
|
944 |
+
# scale the initial noise by the standard deviation required by the scheduler
|
945 |
+
latents = latents * self.scheduler.init_noise_sigma
|
946 |
+
return latents
|
947 |
+
|
948 |
+
def _default_height_width(self, height, width, image):
|
949 |
+
# NOTE: It is possible that a list of images have different
|
950 |
+
# dimensions for each image, so just checking the first image
|
951 |
+
# is not _exactly_ correct, but it is simple.
|
952 |
+
while isinstance(image, list):
|
953 |
+
image = image[0]
|
954 |
+
|
955 |
+
if height is None:
|
956 |
+
if isinstance(image, PIL.Image.Image):
|
957 |
+
height = image.height
|
958 |
+
elif isinstance(image, torch.Tensor):
|
959 |
+
height = image.shape[-2]
|
960 |
+
|
961 |
+
# round down to nearest multiple of `self.adapter.downscale_factor`
|
962 |
+
height = (height // self.adapter.downscale_factor) * self.adapter.downscale_factor
|
963 |
+
|
964 |
+
if width is None:
|
965 |
+
if isinstance(image, PIL.Image.Image):
|
966 |
+
width = image.width
|
967 |
+
elif isinstance(image, torch.Tensor):
|
968 |
+
width = image.shape[-1]
|
969 |
+
|
970 |
+
# round down to nearest multiple of `self.adapter.downscale_factor`
|
971 |
+
width = (width // self.adapter.downscale_factor) * self.adapter.downscale_factor
|
972 |
+
|
973 |
+
return height, width
|
974 |
+
|
975 |
+
|
976 |
+
def prepare_image_latents(
|
977 |
+
self, image, batch_size, num_images_per_prompt, dtype, device, do_classifier_free_guidance, generator=None
|
978 |
+
):
|
979 |
+
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
|
980 |
+
raise ValueError(
|
981 |
+
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
|
982 |
+
)
|
983 |
+
|
984 |
+
image = image.to(device=device, dtype=dtype)
|
985 |
+
|
986 |
+
batch_size = batch_size * num_images_per_prompt
|
987 |
+
|
988 |
+
if image.shape[1] == 4:
|
989 |
+
image_latents = image
|
990 |
+
else:
|
991 |
+
image_latents = retrieve_latents(self.vae.encode(image), sample_mode="argmax")
|
992 |
+
|
993 |
+
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
|
994 |
+
# expand image_latents for batch_size
|
995 |
+
deprecation_message = (
|
996 |
+
f"You have passed {batch_size} text prompts (`prompt`), but only {image_latents.shape[0]} initial"
|
997 |
+
" images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
|
998 |
+
" that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
|
999 |
+
" your script to pass as many initial images as text prompts to suppress this warning."
|
1000 |
+
)
|
1001 |
+
deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
|
1002 |
+
additional_image_per_prompt = batch_size // image_latents.shape[0]
|
1003 |
+
image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
|
1004 |
+
elif batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] != 0:
|
1005 |
+
raise ValueError(
|
1006 |
+
f"Cannot duplicate `image` of batch size {image_latents.shape[0]} to {batch_size} text prompts."
|
1007 |
+
)
|
1008 |
+
else:
|
1009 |
+
image_latents = torch.cat([image_latents], dim=0)
|
1010 |
+
|
1011 |
+
if do_classifier_free_guidance:
|
1012 |
+
uncond_image_latents = torch.zeros_like(image_latents)
|
1013 |
+
image_latents = torch.cat([image_latents, image_latents, uncond_image_latents], dim=0)
|
1014 |
+
|
1015 |
+
return image_latents
|
1016 |
+
|
1017 |
+
@property
|
1018 |
+
def guidance_scale(self):
|
1019 |
+
return self._guidance_scale
|
1020 |
+
|
1021 |
+
@property
|
1022 |
+
def image_guidance_scale(self):
|
1023 |
+
return self._image_guidance_scale
|
1024 |
+
|
1025 |
+
@property
|
1026 |
+
def num_timesteps(self):
|
1027 |
+
return self._num_timesteps
|
1028 |
+
|
1029 |
+
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
1030 |
+
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
|
1031 |
+
# corresponds to doing no classifier free guidance.
|
1032 |
+
@property
|
1033 |
+
def do_classifier_free_guidance(self):
|
1034 |
+
return self.guidance_scale > 1.0 and self.image_guidance_scale >= 1.0
|