Spaces:
Running
on
Zero
Running
on
Zero
add app
Browse files- .gitattributes +2 -0
- .idea/workspace.xml +62 -0
- README.md +5 -6
- app.py +356 -0
- assets/image3_1.jpg +3 -0
- assets/img_0.png +3 -0
- assets/img_1.png +3 -0
- assets/img_2.png +3 -0
- assets/overview.jpg +3 -0
- assets/page1.png +3 -0
- assets/page11.jpg +3 -0
- assets/page4.png +3 -0
- assets/page8.png +3 -0
- assets/vis.jpg +3 -0
- ip_adapter/__init__.py +10 -0
- ip_adapter/attention_processor.py +754 -0
- ip_adapter/ip_adapter.py +1078 -0
- ip_adapter/resampler.py +158 -0
- ip_adapter/utils.py +142 -0
- requirements.txt +18 -0
.gitattributes
CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.jpg filter=lfs diff=lfs merge=lfs -text
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*.png filter=lfs diff=lfs merge=lfs -text
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.idea/workspace.xml
ADDED
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</project>
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README.md
CHANGED
@@ -1,12 +1,11 @@
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---
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title: CSGO
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-
emoji:
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-
colorFrom:
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colorTo:
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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---
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-
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-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: CSGO
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+
emoji: 🏔️
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colorFrom: blue
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colorTo: purple
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sdk: gradio
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sdk_version: 4.26.0
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app_file: app.py
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pinned: false
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license: apache-2.0
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---
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app.py
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1 |
+
import sys
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2 |
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sys.path.append('./')
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3 |
+
import spaces
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4 |
+
import gradio as gr
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5 |
+
import torch
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6 |
+
from ip_adapter.utils import BLOCKS as BLOCKS
|
7 |
+
from ip_adapter.utils import controlnet_BLOCKS as controlnet_BLOCKS
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8 |
+
from ip_adapter.utils import resize_content
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9 |
+
import cv2
|
10 |
+
import numpy as np
|
11 |
+
import random
|
12 |
+
from PIL import Image
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13 |
+
from transformers import AutoImageProcessor, AutoModel
|
14 |
+
from diffusers import (
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15 |
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AutoencoderKL,
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16 |
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ControlNetModel,
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17 |
+
StableDiffusionXLControlNetPipeline,
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18 |
+
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19 |
+
)
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20 |
+
from ip_adapter import CSGO
|
21 |
+
from transformers import BlipProcessor, BlipForConditionalGeneration
|
22 |
+
|
23 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
24 |
+
|
25 |
+
import os
|
26 |
+
os.system("git lfs install")
|
27 |
+
os.system("git clone https://huggingface.co/h94/IP-Adapter")
|
28 |
+
os.system("mv IP-Adapter/sdxl_models sdxl_models")
|
29 |
+
|
30 |
+
base_model_path = "stabilityai/stable-diffusion-xl-base-1.0"
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31 |
+
image_encoder_path = "sdxl_models/image_encoder"
|
32 |
+
csgo_ckpt ='./CSGO/csgo_4_32.bin'
|
33 |
+
pretrained_vae_name_or_path ='madebyollin/sdxl-vae-fp16-fix'
|
34 |
+
controlnet_path = "TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic"
|
35 |
+
weight_dtype = torch.float16
|
36 |
+
|
37 |
+
|
38 |
+
os.system("git clone https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic")
|
39 |
+
os.system("mv TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v2_fp16.safetensors TTPLanet_SDXL_Controlnet_Tile_Realistic/diffusion_pytorch_model.safetensors")
|
40 |
+
os.system('rm TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v1_fp16.safetensors')
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41 |
+
os.system('rm TTPLanet_SDXL_Controlnet_Tile_Realistic/TTPLANET_Controlnet_Tile_realistic_v1_fp16.safetensors')
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42 |
+
controlnet_path = "./TTPLanet_SDXL_Controlnet_Tile_Realistic"
|
43 |
+
|
44 |
+
|
45 |
+
os.system('git clone https://huggingface.co/InstantX/CSGO')
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46 |
+
|
47 |
+
|
48 |
+
|
49 |
+
vae = AutoencoderKL.from_pretrained(pretrained_vae_name_or_path,torch_dtype=torch.float16)
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50 |
+
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16,use_safetensors=True)
|
51 |
+
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
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52 |
+
base_model_path,
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53 |
+
controlnet=controlnet,
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54 |
+
torch_dtype=torch.float16,
|
55 |
+
add_watermarker=False,
|
56 |
+
vae=vae
|
57 |
+
)
|
58 |
+
pipe.enable_vae_tiling()
|
59 |
+
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60 |
+
|
61 |
+
blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
|
62 |
+
blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
|
63 |
+
|
64 |
+
target_content_blocks = BLOCKS['content']
|
65 |
+
target_style_blocks = BLOCKS['style']
|
66 |
+
controlnet_target_content_blocks = controlnet_BLOCKS['content']
|
67 |
+
controlnet_target_style_blocks = controlnet_BLOCKS['style']
|
68 |
+
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69 |
+
csgo = CSGO(pipe, image_encoder_path, csgo_ckpt, device, num_content_tokens=4, num_style_tokens=32,
|
70 |
+
target_content_blocks=target_content_blocks, target_style_blocks=target_style_blocks,
|
71 |
+
controlnet_adapter=True,
|
72 |
+
controlnet_target_content_blocks=controlnet_target_content_blocks,
|
73 |
+
controlnet_target_style_blocks=controlnet_target_style_blocks,
|
74 |
+
content_model_resampler=True,
|
75 |
+
style_model_resampler=True,
|
76 |
+
)
|
77 |
+
|
78 |
+
MAX_SEED = np.iinfo(np.int32).max
|
79 |
+
|
80 |
+
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
|
81 |
+
if randomize_seed:
|
82 |
+
seed = random.randint(0, MAX_SEED)
|
83 |
+
return seed
|
84 |
+
|
85 |
+
|
86 |
+
|
87 |
+
|
88 |
+
|
89 |
+
def get_example():
|
90 |
+
case = [
|
91 |
+
[
|
92 |
+
"./assets/img_0.png",
|
93 |
+
'./assets/img_1.png',
|
94 |
+
"Image-Driven Style Transfer",
|
95 |
+
"there is a small house with a sheep statue on top of it",
|
96 |
+
0.6,
|
97 |
+
1.0,
|
98 |
+
],
|
99 |
+
[
|
100 |
+
None,
|
101 |
+
'./assets/img_1.png',
|
102 |
+
"Text-Driven Style Synthesis",
|
103 |
+
"a cat",
|
104 |
+
0.01,1.0
|
105 |
+
],
|
106 |
+
[
|
107 |
+
None,
|
108 |
+
'./assets/img_2.png',
|
109 |
+
"Text-Driven Style Synthesis",
|
110 |
+
"a building",
|
111 |
+
0.01, 1.0
|
112 |
+
],
|
113 |
+
[
|
114 |
+
"./assets/img_0.png",
|
115 |
+
'./assets/img_1.png',
|
116 |
+
"Text Edit-Driven Style Synthesis",
|
117 |
+
"there is a small house",
|
118 |
+
0.4,
|
119 |
+
1.0
|
120 |
+
],
|
121 |
+
]
|
122 |
+
return case
|
123 |
+
|
124 |
+
|
125 |
+
def run_for_examples(content_image_pil,style_image_pil,target, prompt, scale_c, scale_s):
|
126 |
+
return create_image(
|
127 |
+
content_image_pil=content_image_pil,
|
128 |
+
style_image_pil=style_image_pil,
|
129 |
+
prompt=prompt,
|
130 |
+
scale_c=scale_c,
|
131 |
+
scale_s=scale_s,
|
132 |
+
guidance_scale=7.0,
|
133 |
+
num_samples=3,
|
134 |
+
num_inference_steps=50,
|
135 |
+
seed=42,
|
136 |
+
target=target,
|
137 |
+
)
|
138 |
+
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
|
139 |
+
if randomize_seed:
|
140 |
+
seed = random.randint(0, MAX_SEED)
|
141 |
+
return seed
|
142 |
+
|
143 |
+
def image_grid(imgs, rows, cols):
|
144 |
+
assert len(imgs) == rows * cols
|
145 |
+
|
146 |
+
w, h = imgs[0].size
|
147 |
+
grid = Image.new('RGB', size=(cols * w, rows * h))
|
148 |
+
grid_w, grid_h = grid.size
|
149 |
+
|
150 |
+
for i, img in enumerate(imgs):
|
151 |
+
grid.paste(img, box=(i % cols * w, i // cols * h))
|
152 |
+
return grid
|
153 |
+
@spaces.GPU(enable_queue=True,duration=200)
|
154 |
+
def create_image(content_image_pil,
|
155 |
+
style_image_pil,
|
156 |
+
prompt,
|
157 |
+
scale_c,
|
158 |
+
scale_s,
|
159 |
+
guidance_scale,
|
160 |
+
num_samples,
|
161 |
+
num_inference_steps,
|
162 |
+
seed,
|
163 |
+
target="Image-Driven Style Transfer",
|
164 |
+
):
|
165 |
+
|
166 |
+
|
167 |
+
if content_image_pil is None:
|
168 |
+
content_image_pil = Image.fromarray(
|
169 |
+
np.zeros((1024, 1024, 3), dtype=np.uint8)).convert('RGB')
|
170 |
+
|
171 |
+
if prompt is None:
|
172 |
+
blip_model = blip_model.to(device)
|
173 |
+
inputs = blip_processor(content_image_pil, return_tensors="pt").to(device)
|
174 |
+
out = blip_model.generate(**inputs)
|
175 |
+
prompt = blip_processor.decode(out[0], skip_special_tokens=True)
|
176 |
+
width, height, content_image = resize_content(content_image_pil)
|
177 |
+
style_image = style_image_pil
|
178 |
+
neg_content_prompt='text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry'
|
179 |
+
if target =="Image-Driven Style Transfer":
|
180 |
+
|
181 |
+
images = csgo.generate(pil_content_image=content_image, pil_style_image=style_image,
|
182 |
+
prompt=prompt,
|
183 |
+
negative_prompt=neg_content_prompt,
|
184 |
+
height=height,
|
185 |
+
width=width,
|
186 |
+
content_scale=1.0,
|
187 |
+
style_scale=scale_s,
|
188 |
+
guidance_scale=guidance_scale,
|
189 |
+
num_images_per_prompt=num_samples,
|
190 |
+
num_inference_steps=num_inference_steps,
|
191 |
+
num_samples=1,
|
192 |
+
seed=seed,
|
193 |
+
image=content_image.convert('RGB'),
|
194 |
+
controlnet_conditioning_scale=scale_c,
|
195 |
+
)
|
196 |
+
|
197 |
+
elif target =="Text-Driven Style Synthesis":
|
198 |
+
content_image = Image.fromarray(
|
199 |
+
np.zeros((1024, 1024, 3), dtype=np.uint8)).convert('RGB')
|
200 |
+
|
201 |
+
images = csgo.generate(pil_content_image=content_image, pil_style_image=style_image,
|
202 |
+
prompt=prompt,
|
203 |
+
negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
|
204 |
+
height=height,
|
205 |
+
width=width,
|
206 |
+
content_scale=0.5,
|
207 |
+
style_scale=scale_s,
|
208 |
+
guidance_scale=7,
|
209 |
+
num_images_per_prompt=num_samples,
|
210 |
+
num_inference_steps=num_inference_steps,
|
211 |
+
num_samples=1,
|
212 |
+
seed=42,
|
213 |
+
image=content_image.convert('RGB'),
|
214 |
+
controlnet_conditioning_scale=scale_c,
|
215 |
+
)
|
216 |
+
elif target =="Text Edit-Driven Style Synthesis":
|
217 |
+
|
218 |
+
|
219 |
+
images = csgo.generate(pil_content_image=content_image, pil_style_image=style_image,
|
220 |
+
prompt=prompt,
|
221 |
+
negative_prompt=neg_content_prompt,
|
222 |
+
height=height,
|
223 |
+
width=width,
|
224 |
+
content_scale=1.0,
|
225 |
+
style_scale=scale_s,
|
226 |
+
guidance_scale=guidance_scale,
|
227 |
+
num_images_per_prompt=num_samples,
|
228 |
+
num_inference_steps=num_inference_steps,
|
229 |
+
num_samples=1,
|
230 |
+
seed=seed,
|
231 |
+
image=content_image.convert('RGB'),
|
232 |
+
controlnet_conditioning_scale=scale_c,
|
233 |
+
)
|
234 |
+
|
235 |
+
return [image_grid(images, 1, num_samples)]
|
236 |
+
|
237 |
+
|
238 |
+
def pil_to_cv2(image_pil):
|
239 |
+
image_np = np.array(image_pil)
|
240 |
+
image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
|
241 |
+
return image_cv2
|
242 |
+
|
243 |
+
|
244 |
+
# Description
|
245 |
+
title = r"""
|
246 |
+
<h1 align="center">CSGO: Content-Style Composition in Text-to-Image Generation</h1>
|
247 |
+
"""
|
248 |
+
|
249 |
+
description = r"""
|
250 |
+
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/instantX-research/CSGO' target='_blank'><b>CSGO: Content-Style Composition in Text-to-Image Generation</b></a>.<br>
|
251 |
+
How to use:<br>
|
252 |
+
1. Upload a content image if you want to use image-driven style transfer.
|
253 |
+
2. Upload a style image.
|
254 |
+
3. Sets the type of task to perform, by default image-driven style transfer is performed. Options are <b>Image-driven style transfer, Text-driven style synthesis, and Text editing-driven style synthesis<b>.
|
255 |
+
4. <b>If you choose a text-driven task, enter your desired prompt<b>.
|
256 |
+
5.If you don't provide a prompt, the default is to use the BLIP model to generate the caption. We suggest that by providing detailed prompts for Content images, CSGO is able to effectively guarantee content.
|
257 |
+
6. Click the <b>Submit</b> button to begin customization.
|
258 |
+
7. Share your stylized photo with your friends and enjoy! 😊
|
259 |
+
|
260 |
+
Advanced usage:<br>
|
261 |
+
1. Click advanced options.
|
262 |
+
2. Choose different guidance and steps.
|
263 |
+
"""
|
264 |
+
|
265 |
+
article = r"""
|
266 |
+
---
|
267 |
+
📝 **Tips**
|
268 |
+
In CSGO, the more accurate the text prompts for content images, the better the content retention.
|
269 |
+
Text-driven style synthesis and text-edit-driven style synthesis are expected to be more stable in the next release.
|
270 |
+
---
|
271 |
+
📝 **Citation**
|
272 |
+
<br>
|
273 |
+
If our work is helpful for your research or applications, please cite us via:
|
274 |
+
```bibtex
|
275 |
+
@article{xing2024csgo,
|
276 |
+
title={CSGO: Content-Style Composition in Text-to-Image Generation},
|
277 |
+
author={Peng Xing and Haofan Wang and Yanpeng Sun and Qixun Wang and Xu Bai and Hao Ai and Renyuan Huang and Zechao Li},
|
278 |
+
year={2024},
|
279 |
+
journal = {arXiv 2408.16766},
|
280 |
+
}
|
281 |
+
```
|
282 |
+
📧 **Contact**
|
283 |
+
<br>
|
284 |
+
If you have any questions, please feel free to open an issue or directly reach us out at <b>[email protected]</b>.
|
285 |
+
"""
|
286 |
+
|
287 |
+
block = gr.Blocks(css="footer {visibility: hidden}").queue(max_size=10, api_open=False)
|
288 |
+
with block:
|
289 |
+
# description
|
290 |
+
gr.Markdown(title)
|
291 |
+
gr.Markdown(description)
|
292 |
+
|
293 |
+
with gr.Tabs():
|
294 |
+
with gr.Row():
|
295 |
+
with gr.Column():
|
296 |
+
with gr.Row():
|
297 |
+
with gr.Column():
|
298 |
+
content_image_pil = gr.Image(label="Content Image (optional)", type='pil')
|
299 |
+
style_image_pil = gr.Image(label="Style Image", type='pil')
|
300 |
+
|
301 |
+
target = gr.Radio(["Image-Driven Style Transfer", "Text-Driven Style Synthesis", "Text Edit-Driven Style Synthesis"],
|
302 |
+
value="Image-Driven Style Transfer",
|
303 |
+
label="task")
|
304 |
+
|
305 |
+
prompt = gr.Textbox(label="Prompt",
|
306 |
+
value="there is a small house with a sheep statue on top of it")
|
307 |
+
|
308 |
+
|
309 |
+
scale_c = gr.Slider(minimum=0, maximum=2.0, step=0.01, value=0.6, label="Content Scale")
|
310 |
+
scale_s = gr.Slider(minimum=0, maximum=2.0, step=0.01, value=1.0, label="Style Scale")
|
311 |
+
with gr.Accordion(open=False, label="Advanced Options"):
|
312 |
+
|
313 |
+
guidance_scale = gr.Slider(minimum=1, maximum=15.0, step=0.01, value=7.0, label="guidance scale")
|
314 |
+
num_samples = gr.Slider(minimum=1, maximum=4.0, step=1.0, value=1.0, label="num samples")
|
315 |
+
num_inference_steps = gr.Slider(minimum=5, maximum=100.0, step=1.0, value=50,
|
316 |
+
label="num inference steps")
|
317 |
+
seed = gr.Slider(minimum=-1000000, maximum=1000000, value=1, step=1, label="Seed Value")
|
318 |
+
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
|
319 |
+
|
320 |
+
generate_button = gr.Button("Generate Image")
|
321 |
+
|
322 |
+
with gr.Column():
|
323 |
+
generated_image = gr.Gallery(label="Generated Image")
|
324 |
+
|
325 |
+
generate_button.click(
|
326 |
+
fn=randomize_seed_fn,
|
327 |
+
inputs=[seed, randomize_seed],
|
328 |
+
outputs=seed,
|
329 |
+
queue=False,
|
330 |
+
api_name=False,
|
331 |
+
).then(
|
332 |
+
fn=create_image,
|
333 |
+
inputs=[content_image_pil,
|
334 |
+
style_image_pil,
|
335 |
+
prompt,
|
336 |
+
|
337 |
+
scale_c,
|
338 |
+
scale_s,
|
339 |
+
guidance_scale,
|
340 |
+
num_samples,
|
341 |
+
num_inference_steps,
|
342 |
+
seed,
|
343 |
+
target,],
|
344 |
+
outputs=[generated_image])
|
345 |
+
|
346 |
+
gr.Examples(
|
347 |
+
examples=get_example(),
|
348 |
+
inputs=[content_image_pil,style_image_pil,target, prompt, scale_c, scale_s],
|
349 |
+
fn=run_for_examples,
|
350 |
+
outputs=[generated_image],
|
351 |
+
cache_examples=True,
|
352 |
+
)
|
353 |
+
|
354 |
+
gr.Markdown(article)
|
355 |
+
|
356 |
+
block.launch(server_name="0.0.0.0", server_port=1234)
|
assets/image3_1.jpg
ADDED
Git LFS Details
|
assets/img_0.png
ADDED
Git LFS Details
|
assets/img_1.png
ADDED
Git LFS Details
|
assets/img_2.png
ADDED
Git LFS Details
|
assets/overview.jpg
ADDED
Git LFS Details
|
assets/page1.png
ADDED
Git LFS Details
|
assets/page11.jpg
ADDED
Git LFS Details
|
assets/page4.png
ADDED
Git LFS Details
|
assets/page8.png
ADDED
Git LFS Details
|
assets/vis.jpg
ADDED
Git LFS Details
|
ip_adapter/__init__.py
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from .ip_adapter import IPAdapter, IPAdapterPlus, IPAdapterPlusXL, IPAdapterXL, IPAdapterFull,IPAdapterXL_CS,IPAdapter_CS
|
2 |
+
from .ip_adapter import CSGO
|
3 |
+
__all__ = [
|
4 |
+
"IPAdapter",
|
5 |
+
"IPAdapterPlus",
|
6 |
+
"IPAdapterPlusXL",
|
7 |
+
"IPAdapterXL",
|
8 |
+
"CSGO"
|
9 |
+
"IPAdapterFull",
|
10 |
+
]
|
ip_adapter/attention_processor.py
ADDED
@@ -0,0 +1,754 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
1 |
+
# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
import torch.nn.functional as F
|
5 |
+
|
6 |
+
|
7 |
+
class AttnProcessor(nn.Module):
|
8 |
+
r"""
|
9 |
+
Default processor for performing attention-related computations.
|
10 |
+
"""
|
11 |
+
|
12 |
+
def __init__(
|
13 |
+
self,
|
14 |
+
hidden_size=None,
|
15 |
+
cross_attention_dim=None,
|
16 |
+
save_in_unet='down',
|
17 |
+
atten_control=None,
|
18 |
+
):
|
19 |
+
super().__init__()
|
20 |
+
self.atten_control = atten_control
|
21 |
+
self.save_in_unet = save_in_unet
|
22 |
+
|
23 |
+
def __call__(
|
24 |
+
self,
|
25 |
+
attn,
|
26 |
+
hidden_states,
|
27 |
+
encoder_hidden_states=None,
|
28 |
+
attention_mask=None,
|
29 |
+
temb=None,
|
30 |
+
):
|
31 |
+
residual = hidden_states
|
32 |
+
|
33 |
+
if attn.spatial_norm is not None:
|
34 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
35 |
+
|
36 |
+
input_ndim = hidden_states.ndim
|
37 |
+
|
38 |
+
if input_ndim == 4:
|
39 |
+
batch_size, channel, height, width = hidden_states.shape
|
40 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
41 |
+
|
42 |
+
batch_size, sequence_length, _ = (
|
43 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
44 |
+
)
|
45 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
46 |
+
|
47 |
+
if attn.group_norm is not None:
|
48 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
49 |
+
|
50 |
+
query = attn.to_q(hidden_states)
|
51 |
+
|
52 |
+
if encoder_hidden_states is None:
|
53 |
+
encoder_hidden_states = hidden_states
|
54 |
+
elif attn.norm_cross:
|
55 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
56 |
+
|
57 |
+
key = attn.to_k(encoder_hidden_states)
|
58 |
+
value = attn.to_v(encoder_hidden_states)
|
59 |
+
|
60 |
+
query = attn.head_to_batch_dim(query)
|
61 |
+
key = attn.head_to_batch_dim(key)
|
62 |
+
value = attn.head_to_batch_dim(value)
|
63 |
+
|
64 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
65 |
+
hidden_states = torch.bmm(attention_probs, value)
|
66 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
67 |
+
|
68 |
+
# linear proj
|
69 |
+
hidden_states = attn.to_out[0](hidden_states)
|
70 |
+
# dropout
|
71 |
+
hidden_states = attn.to_out[1](hidden_states)
|
72 |
+
|
73 |
+
if input_ndim == 4:
|
74 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
75 |
+
|
76 |
+
if attn.residual_connection:
|
77 |
+
hidden_states = hidden_states + residual
|
78 |
+
|
79 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
80 |
+
|
81 |
+
return hidden_states
|
82 |
+
|
83 |
+
|
84 |
+
class IPAttnProcessor(nn.Module):
|
85 |
+
r"""
|
86 |
+
Attention processor for IP-Adapater.
|
87 |
+
Args:
|
88 |
+
hidden_size (`int`):
|
89 |
+
The hidden size of the attention layer.
|
90 |
+
cross_attention_dim (`int`):
|
91 |
+
The number of channels in the `encoder_hidden_states`.
|
92 |
+
scale (`float`, defaults to 1.0):
|
93 |
+
the weight scale of image prompt.
|
94 |
+
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
|
95 |
+
The context length of the image features.
|
96 |
+
"""
|
97 |
+
|
98 |
+
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, skip=False,save_in_unet='down', atten_control=None):
|
99 |
+
super().__init__()
|
100 |
+
|
101 |
+
self.hidden_size = hidden_size
|
102 |
+
self.cross_attention_dim = cross_attention_dim
|
103 |
+
self.scale = scale
|
104 |
+
self.num_tokens = num_tokens
|
105 |
+
self.skip = skip
|
106 |
+
|
107 |
+
self.atten_control = atten_control
|
108 |
+
self.save_in_unet = save_in_unet
|
109 |
+
|
110 |
+
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
111 |
+
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
112 |
+
|
113 |
+
def __call__(
|
114 |
+
self,
|
115 |
+
attn,
|
116 |
+
hidden_states,
|
117 |
+
encoder_hidden_states=None,
|
118 |
+
attention_mask=None,
|
119 |
+
temb=None,
|
120 |
+
):
|
121 |
+
residual = hidden_states
|
122 |
+
|
123 |
+
if attn.spatial_norm is not None:
|
124 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
125 |
+
|
126 |
+
input_ndim = hidden_states.ndim
|
127 |
+
|
128 |
+
if input_ndim == 4:
|
129 |
+
batch_size, channel, height, width = hidden_states.shape
|
130 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
131 |
+
|
132 |
+
batch_size, sequence_length, _ = (
|
133 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
134 |
+
)
|
135 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
136 |
+
|
137 |
+
if attn.group_norm is not None:
|
138 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
139 |
+
|
140 |
+
query = attn.to_q(hidden_states)
|
141 |
+
|
142 |
+
if encoder_hidden_states is None:
|
143 |
+
encoder_hidden_states = hidden_states
|
144 |
+
else:
|
145 |
+
# get encoder_hidden_states, ip_hidden_states
|
146 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
147 |
+
encoder_hidden_states, ip_hidden_states = (
|
148 |
+
encoder_hidden_states[:, :end_pos, :],
|
149 |
+
encoder_hidden_states[:, end_pos:, :],
|
150 |
+
)
|
151 |
+
if attn.norm_cross:
|
152 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
153 |
+
|
154 |
+
key = attn.to_k(encoder_hidden_states)
|
155 |
+
value = attn.to_v(encoder_hidden_states)
|
156 |
+
|
157 |
+
query = attn.head_to_batch_dim(query)
|
158 |
+
key = attn.head_to_batch_dim(key)
|
159 |
+
value = attn.head_to_batch_dim(value)
|
160 |
+
|
161 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
162 |
+
hidden_states = torch.bmm(attention_probs, value)
|
163 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
164 |
+
|
165 |
+
if not self.skip:
|
166 |
+
# for ip-adapter
|
167 |
+
ip_key = self.to_k_ip(ip_hidden_states)
|
168 |
+
ip_value = self.to_v_ip(ip_hidden_states)
|
169 |
+
|
170 |
+
ip_key = attn.head_to_batch_dim(ip_key)
|
171 |
+
ip_value = attn.head_to_batch_dim(ip_value)
|
172 |
+
|
173 |
+
ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
|
174 |
+
self.attn_map = ip_attention_probs
|
175 |
+
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
|
176 |
+
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
|
177 |
+
|
178 |
+
hidden_states = hidden_states + self.scale * ip_hidden_states
|
179 |
+
|
180 |
+
# linear proj
|
181 |
+
hidden_states = attn.to_out[0](hidden_states)
|
182 |
+
# dropout
|
183 |
+
hidden_states = attn.to_out[1](hidden_states)
|
184 |
+
|
185 |
+
if input_ndim == 4:
|
186 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
187 |
+
|
188 |
+
if attn.residual_connection:
|
189 |
+
hidden_states = hidden_states + residual
|
190 |
+
|
191 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
192 |
+
|
193 |
+
return hidden_states
|
194 |
+
|
195 |
+
|
196 |
+
class AttnProcessor2_0(torch.nn.Module):
|
197 |
+
r"""
|
198 |
+
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
199 |
+
"""
|
200 |
+
|
201 |
+
def __init__(
|
202 |
+
self,
|
203 |
+
hidden_size=None,
|
204 |
+
cross_attention_dim=None,
|
205 |
+
save_in_unet='down',
|
206 |
+
atten_control=None,
|
207 |
+
):
|
208 |
+
super().__init__()
|
209 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
210 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
211 |
+
self.atten_control = atten_control
|
212 |
+
self.save_in_unet = save_in_unet
|
213 |
+
|
214 |
+
def __call__(
|
215 |
+
self,
|
216 |
+
attn,
|
217 |
+
hidden_states,
|
218 |
+
encoder_hidden_states=None,
|
219 |
+
attention_mask=None,
|
220 |
+
temb=None,
|
221 |
+
):
|
222 |
+
residual = hidden_states
|
223 |
+
|
224 |
+
if attn.spatial_norm is not None:
|
225 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
226 |
+
|
227 |
+
input_ndim = hidden_states.ndim
|
228 |
+
|
229 |
+
if input_ndim == 4:
|
230 |
+
batch_size, channel, height, width = hidden_states.shape
|
231 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
232 |
+
|
233 |
+
batch_size, sequence_length, _ = (
|
234 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
235 |
+
)
|
236 |
+
|
237 |
+
if attention_mask is not None:
|
238 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
239 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
240 |
+
# (batch, heads, source_length, target_length)
|
241 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
242 |
+
|
243 |
+
if attn.group_norm is not None:
|
244 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
245 |
+
|
246 |
+
query = attn.to_q(hidden_states)
|
247 |
+
|
248 |
+
if encoder_hidden_states is None:
|
249 |
+
encoder_hidden_states = hidden_states
|
250 |
+
elif attn.norm_cross:
|
251 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
252 |
+
|
253 |
+
key = attn.to_k(encoder_hidden_states)
|
254 |
+
value = attn.to_v(encoder_hidden_states)
|
255 |
+
|
256 |
+
inner_dim = key.shape[-1]
|
257 |
+
head_dim = inner_dim // attn.heads
|
258 |
+
|
259 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
260 |
+
|
261 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
262 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
263 |
+
|
264 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
265 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
266 |
+
hidden_states = F.scaled_dot_product_attention(
|
267 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
268 |
+
)
|
269 |
+
|
270 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
271 |
+
hidden_states = hidden_states.to(query.dtype)
|
272 |
+
|
273 |
+
# linear proj
|
274 |
+
hidden_states = attn.to_out[0](hidden_states)
|
275 |
+
# dropout
|
276 |
+
hidden_states = attn.to_out[1](hidden_states)
|
277 |
+
|
278 |
+
if input_ndim == 4:
|
279 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
280 |
+
|
281 |
+
if attn.residual_connection:
|
282 |
+
hidden_states = hidden_states + residual
|
283 |
+
|
284 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
285 |
+
|
286 |
+
return hidden_states
|
287 |
+
|
288 |
+
|
289 |
+
class IPAttnProcessor2_0(torch.nn.Module):
|
290 |
+
r"""
|
291 |
+
Attention processor for IP-Adapater for PyTorch 2.0.
|
292 |
+
Args:
|
293 |
+
hidden_size (`int`):
|
294 |
+
The hidden size of the attention layer.
|
295 |
+
cross_attention_dim (`int`):
|
296 |
+
The number of channels in the `encoder_hidden_states`.
|
297 |
+
scale (`float`, defaults to 1.0):
|
298 |
+
the weight scale of image prompt.
|
299 |
+
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
|
300 |
+
The context length of the image features.
|
301 |
+
"""
|
302 |
+
|
303 |
+
def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, skip=False,save_in_unet='down', atten_control=None):
|
304 |
+
super().__init__()
|
305 |
+
|
306 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
307 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
308 |
+
|
309 |
+
self.hidden_size = hidden_size
|
310 |
+
self.cross_attention_dim = cross_attention_dim
|
311 |
+
self.scale = scale
|
312 |
+
self.num_tokens = num_tokens
|
313 |
+
self.skip = skip
|
314 |
+
|
315 |
+
self.atten_control = atten_control
|
316 |
+
self.save_in_unet = save_in_unet
|
317 |
+
|
318 |
+
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
319 |
+
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
320 |
+
|
321 |
+
def __call__(
|
322 |
+
self,
|
323 |
+
attn,
|
324 |
+
hidden_states,
|
325 |
+
encoder_hidden_states=None,
|
326 |
+
attention_mask=None,
|
327 |
+
temb=None,
|
328 |
+
):
|
329 |
+
residual = hidden_states
|
330 |
+
|
331 |
+
if attn.spatial_norm is not None:
|
332 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
333 |
+
|
334 |
+
input_ndim = hidden_states.ndim
|
335 |
+
|
336 |
+
if input_ndim == 4:
|
337 |
+
batch_size, channel, height, width = hidden_states.shape
|
338 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
339 |
+
|
340 |
+
batch_size, sequence_length, _ = (
|
341 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
342 |
+
)
|
343 |
+
|
344 |
+
if attention_mask is not None:
|
345 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
346 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
347 |
+
# (batch, heads, source_length, target_length)
|
348 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
349 |
+
|
350 |
+
if attn.group_norm is not None:
|
351 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
352 |
+
|
353 |
+
query = attn.to_q(hidden_states)
|
354 |
+
|
355 |
+
if encoder_hidden_states is None:
|
356 |
+
encoder_hidden_states = hidden_states
|
357 |
+
else:
|
358 |
+
# get encoder_hidden_states, ip_hidden_states
|
359 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
360 |
+
encoder_hidden_states, ip_hidden_states = (
|
361 |
+
encoder_hidden_states[:, :end_pos, :],
|
362 |
+
encoder_hidden_states[:, end_pos:, :],
|
363 |
+
)
|
364 |
+
if attn.norm_cross:
|
365 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
366 |
+
|
367 |
+
key = attn.to_k(encoder_hidden_states)
|
368 |
+
value = attn.to_v(encoder_hidden_states)
|
369 |
+
|
370 |
+
inner_dim = key.shape[-1]
|
371 |
+
head_dim = inner_dim // attn.heads
|
372 |
+
|
373 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
374 |
+
|
375 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
376 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
377 |
+
|
378 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
379 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
380 |
+
hidden_states = F.scaled_dot_product_attention(
|
381 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
382 |
+
)
|
383 |
+
|
384 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
385 |
+
hidden_states = hidden_states.to(query.dtype)
|
386 |
+
|
387 |
+
if not self.skip:
|
388 |
+
# for ip-adapter
|
389 |
+
ip_key = self.to_k_ip(ip_hidden_states)
|
390 |
+
ip_value = self.to_v_ip(ip_hidden_states)
|
391 |
+
|
392 |
+
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
393 |
+
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
394 |
+
|
395 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
396 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
397 |
+
ip_hidden_states = F.scaled_dot_product_attention(
|
398 |
+
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
399 |
+
)
|
400 |
+
with torch.no_grad():
|
401 |
+
self.attn_map = query @ ip_key.transpose(-2, -1).softmax(dim=-1)
|
402 |
+
#print(self.attn_map.shape)
|
403 |
+
|
404 |
+
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
405 |
+
ip_hidden_states = ip_hidden_states.to(query.dtype)
|
406 |
+
|
407 |
+
hidden_states = hidden_states + self.scale * ip_hidden_states
|
408 |
+
|
409 |
+
# linear proj
|
410 |
+
hidden_states = attn.to_out[0](hidden_states)
|
411 |
+
# dropout
|
412 |
+
hidden_states = attn.to_out[1](hidden_states)
|
413 |
+
|
414 |
+
if input_ndim == 4:
|
415 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
416 |
+
|
417 |
+
if attn.residual_connection:
|
418 |
+
hidden_states = hidden_states + residual
|
419 |
+
|
420 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
421 |
+
|
422 |
+
return hidden_states
|
423 |
+
|
424 |
+
|
425 |
+
class IP_CS_AttnProcessor2_0(torch.nn.Module):
|
426 |
+
r"""
|
427 |
+
Attention processor for IP-Adapater for PyTorch 2.0.
|
428 |
+
Args:
|
429 |
+
hidden_size (`int`):
|
430 |
+
The hidden size of the attention layer.
|
431 |
+
cross_attention_dim (`int`):
|
432 |
+
The number of channels in the `encoder_hidden_states`.
|
433 |
+
scale (`float`, defaults to 1.0):
|
434 |
+
the weight scale of image prompt.
|
435 |
+
num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
|
436 |
+
The context length of the image features.
|
437 |
+
"""
|
438 |
+
|
439 |
+
def __init__(self, hidden_size, cross_attention_dim=None, content_scale=1.0,style_scale=1.0, num_content_tokens=4,num_style_tokens=4,
|
440 |
+
skip=False,content=False, style=False):
|
441 |
+
super().__init__()
|
442 |
+
|
443 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
444 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
445 |
+
|
446 |
+
self.hidden_size = hidden_size
|
447 |
+
self.cross_attention_dim = cross_attention_dim
|
448 |
+
self.content_scale = content_scale
|
449 |
+
self.style_scale = style_scale
|
450 |
+
self.num_content_tokens = num_content_tokens
|
451 |
+
self.num_style_tokens = num_style_tokens
|
452 |
+
self.skip = skip
|
453 |
+
|
454 |
+
self.content = content
|
455 |
+
self.style = style
|
456 |
+
|
457 |
+
if self.content or self.style:
|
458 |
+
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
459 |
+
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
460 |
+
self.to_k_ip_content =None
|
461 |
+
self.to_v_ip_content =None
|
462 |
+
|
463 |
+
def set_content_ipa(self,content_scale=1.0):
|
464 |
+
|
465 |
+
self.to_k_ip_content = nn.Linear(self.cross_attention_dim or self.hidden_size, self.hidden_size, bias=False)
|
466 |
+
self.to_v_ip_content = nn.Linear(self.cross_attention_dim or self.hidden_size, self.hidden_size, bias=False)
|
467 |
+
self.content_scale=content_scale
|
468 |
+
self.content =True
|
469 |
+
|
470 |
+
def __call__(
|
471 |
+
self,
|
472 |
+
attn,
|
473 |
+
hidden_states,
|
474 |
+
encoder_hidden_states=None,
|
475 |
+
attention_mask=None,
|
476 |
+
temb=None,
|
477 |
+
):
|
478 |
+
residual = hidden_states
|
479 |
+
|
480 |
+
if attn.spatial_norm is not None:
|
481 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
482 |
+
|
483 |
+
input_ndim = hidden_states.ndim
|
484 |
+
|
485 |
+
if input_ndim == 4:
|
486 |
+
batch_size, channel, height, width = hidden_states.shape
|
487 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
488 |
+
|
489 |
+
batch_size, sequence_length, _ = (
|
490 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
491 |
+
)
|
492 |
+
|
493 |
+
if attention_mask is not None:
|
494 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
495 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
496 |
+
# (batch, heads, source_length, target_length)
|
497 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
498 |
+
|
499 |
+
if attn.group_norm is not None:
|
500 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
501 |
+
|
502 |
+
query = attn.to_q(hidden_states)
|
503 |
+
|
504 |
+
if encoder_hidden_states is None:
|
505 |
+
encoder_hidden_states = hidden_states
|
506 |
+
else:
|
507 |
+
# get encoder_hidden_states, ip_hidden_states
|
508 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_content_tokens-self.num_style_tokens
|
509 |
+
encoder_hidden_states, ip_content_hidden_states,ip_style_hidden_states = (
|
510 |
+
encoder_hidden_states[:, :end_pos, :],
|
511 |
+
encoder_hidden_states[:, end_pos:end_pos + self.num_content_tokens, :],
|
512 |
+
encoder_hidden_states[:, end_pos + self.num_content_tokens:, :],
|
513 |
+
)
|
514 |
+
if attn.norm_cross:
|
515 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
516 |
+
|
517 |
+
key = attn.to_k(encoder_hidden_states)
|
518 |
+
value = attn.to_v(encoder_hidden_states)
|
519 |
+
|
520 |
+
inner_dim = key.shape[-1]
|
521 |
+
head_dim = inner_dim // attn.heads
|
522 |
+
|
523 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
524 |
+
|
525 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
526 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
527 |
+
|
528 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
529 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
530 |
+
hidden_states = F.scaled_dot_product_attention(
|
531 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
532 |
+
)
|
533 |
+
|
534 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
535 |
+
hidden_states = hidden_states.to(query.dtype)
|
536 |
+
|
537 |
+
if not self.skip and self.content is True:
|
538 |
+
# print('content#####################################################')
|
539 |
+
# for ip-content-adapter
|
540 |
+
if self.to_k_ip_content is None:
|
541 |
+
|
542 |
+
ip_content_key = self.to_k_ip(ip_content_hidden_states)
|
543 |
+
ip_content_value = self.to_v_ip(ip_content_hidden_states)
|
544 |
+
else:
|
545 |
+
ip_content_key = self.to_k_ip_content(ip_content_hidden_states)
|
546 |
+
ip_content_value = self.to_v_ip_content(ip_content_hidden_states)
|
547 |
+
|
548 |
+
ip_content_key = ip_content_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
549 |
+
ip_content_value = ip_content_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
550 |
+
|
551 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
552 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
553 |
+
ip_content_hidden_states = F.scaled_dot_product_attention(
|
554 |
+
query, ip_content_key, ip_content_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
555 |
+
)
|
556 |
+
|
557 |
+
|
558 |
+
ip_content_hidden_states = ip_content_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
559 |
+
ip_content_hidden_states = ip_content_hidden_states.to(query.dtype)
|
560 |
+
|
561 |
+
|
562 |
+
hidden_states = hidden_states + self.content_scale * ip_content_hidden_states
|
563 |
+
|
564 |
+
if not self.skip and self.style is True:
|
565 |
+
# for ip-style-adapter
|
566 |
+
ip_style_key = self.to_k_ip(ip_style_hidden_states)
|
567 |
+
ip_style_value = self.to_v_ip(ip_style_hidden_states)
|
568 |
+
|
569 |
+
ip_style_key = ip_style_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
570 |
+
ip_style_value = ip_style_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
571 |
+
|
572 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
573 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
574 |
+
ip_style_hidden_states = F.scaled_dot_product_attention(
|
575 |
+
query, ip_style_key, ip_style_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
576 |
+
)
|
577 |
+
|
578 |
+
ip_style_hidden_states = ip_style_hidden_states.transpose(1, 2).reshape(batch_size, -1,
|
579 |
+
attn.heads * head_dim)
|
580 |
+
ip_style_hidden_states = ip_style_hidden_states.to(query.dtype)
|
581 |
+
|
582 |
+
hidden_states = hidden_states + self.style_scale * ip_style_hidden_states
|
583 |
+
|
584 |
+
# linear proj
|
585 |
+
hidden_states = attn.to_out[0](hidden_states)
|
586 |
+
# dropout
|
587 |
+
hidden_states = attn.to_out[1](hidden_states)
|
588 |
+
|
589 |
+
if input_ndim == 4:
|
590 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
591 |
+
|
592 |
+
if attn.residual_connection:
|
593 |
+
hidden_states = hidden_states + residual
|
594 |
+
|
595 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
596 |
+
|
597 |
+
return hidden_states
|
598 |
+
|
599 |
+
## for controlnet
|
600 |
+
class CNAttnProcessor:
|
601 |
+
r"""
|
602 |
+
Default processor for performing attention-related computations.
|
603 |
+
"""
|
604 |
+
|
605 |
+
def __init__(self, num_tokens=4,save_in_unet='down',atten_control=None):
|
606 |
+
self.num_tokens = num_tokens
|
607 |
+
self.atten_control = atten_control
|
608 |
+
self.save_in_unet = save_in_unet
|
609 |
+
|
610 |
+
def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
|
611 |
+
residual = hidden_states
|
612 |
+
|
613 |
+
if attn.spatial_norm is not None:
|
614 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
615 |
+
|
616 |
+
input_ndim = hidden_states.ndim
|
617 |
+
|
618 |
+
if input_ndim == 4:
|
619 |
+
batch_size, channel, height, width = hidden_states.shape
|
620 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
621 |
+
|
622 |
+
batch_size, sequence_length, _ = (
|
623 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
624 |
+
)
|
625 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
626 |
+
|
627 |
+
if attn.group_norm is not None:
|
628 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
629 |
+
|
630 |
+
query = attn.to_q(hidden_states)
|
631 |
+
|
632 |
+
if encoder_hidden_states is None:
|
633 |
+
encoder_hidden_states = hidden_states
|
634 |
+
else:
|
635 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
636 |
+
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text
|
637 |
+
if attn.norm_cross:
|
638 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
639 |
+
|
640 |
+
key = attn.to_k(encoder_hidden_states)
|
641 |
+
value = attn.to_v(encoder_hidden_states)
|
642 |
+
|
643 |
+
query = attn.head_to_batch_dim(query)
|
644 |
+
key = attn.head_to_batch_dim(key)
|
645 |
+
value = attn.head_to_batch_dim(value)
|
646 |
+
|
647 |
+
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
648 |
+
hidden_states = torch.bmm(attention_probs, value)
|
649 |
+
hidden_states = attn.batch_to_head_dim(hidden_states)
|
650 |
+
|
651 |
+
# linear proj
|
652 |
+
hidden_states = attn.to_out[0](hidden_states)
|
653 |
+
# dropout
|
654 |
+
hidden_states = attn.to_out[1](hidden_states)
|
655 |
+
|
656 |
+
if input_ndim == 4:
|
657 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
658 |
+
|
659 |
+
if attn.residual_connection:
|
660 |
+
hidden_states = hidden_states + residual
|
661 |
+
|
662 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
663 |
+
|
664 |
+
return hidden_states
|
665 |
+
|
666 |
+
|
667 |
+
class CNAttnProcessor2_0:
|
668 |
+
r"""
|
669 |
+
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
670 |
+
"""
|
671 |
+
|
672 |
+
def __init__(self, num_tokens=4, save_in_unet='down', atten_control=None):
|
673 |
+
if not hasattr(F, "scaled_dot_product_attention"):
|
674 |
+
raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
675 |
+
self.num_tokens = num_tokens
|
676 |
+
self.atten_control = atten_control
|
677 |
+
self.save_in_unet = save_in_unet
|
678 |
+
|
679 |
+
def __call__(
|
680 |
+
self,
|
681 |
+
attn,
|
682 |
+
hidden_states,
|
683 |
+
encoder_hidden_states=None,
|
684 |
+
attention_mask=None,
|
685 |
+
temb=None,
|
686 |
+
):
|
687 |
+
residual = hidden_states
|
688 |
+
|
689 |
+
if attn.spatial_norm is not None:
|
690 |
+
hidden_states = attn.spatial_norm(hidden_states, temb)
|
691 |
+
|
692 |
+
input_ndim = hidden_states.ndim
|
693 |
+
|
694 |
+
if input_ndim == 4:
|
695 |
+
batch_size, channel, height, width = hidden_states.shape
|
696 |
+
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
|
697 |
+
|
698 |
+
batch_size, sequence_length, _ = (
|
699 |
+
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
|
700 |
+
)
|
701 |
+
|
702 |
+
if attention_mask is not None:
|
703 |
+
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
|
704 |
+
# scaled_dot_product_attention expects attention_mask shape to be
|
705 |
+
# (batch, heads, source_length, target_length)
|
706 |
+
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
|
707 |
+
|
708 |
+
if attn.group_norm is not None:
|
709 |
+
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
|
710 |
+
|
711 |
+
query = attn.to_q(hidden_states)
|
712 |
+
|
713 |
+
if encoder_hidden_states is None:
|
714 |
+
encoder_hidden_states = hidden_states
|
715 |
+
else:
|
716 |
+
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
|
717 |
+
encoder_hidden_states = encoder_hidden_states[:, :end_pos] # only use text
|
718 |
+
if attn.norm_cross:
|
719 |
+
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
|
720 |
+
|
721 |
+
key = attn.to_k(encoder_hidden_states)
|
722 |
+
value = attn.to_v(encoder_hidden_states)
|
723 |
+
|
724 |
+
inner_dim = key.shape[-1]
|
725 |
+
head_dim = inner_dim // attn.heads
|
726 |
+
|
727 |
+
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
728 |
+
|
729 |
+
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
730 |
+
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
731 |
+
|
732 |
+
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
733 |
+
# TODO: add support for attn.scale when we move to Torch 2.1
|
734 |
+
hidden_states = F.scaled_dot_product_attention(
|
735 |
+
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
736 |
+
)
|
737 |
+
|
738 |
+
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
739 |
+
hidden_states = hidden_states.to(query.dtype)
|
740 |
+
|
741 |
+
# linear proj
|
742 |
+
hidden_states = attn.to_out[0](hidden_states)
|
743 |
+
# dropout
|
744 |
+
hidden_states = attn.to_out[1](hidden_states)
|
745 |
+
|
746 |
+
if input_ndim == 4:
|
747 |
+
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
|
748 |
+
|
749 |
+
if attn.residual_connection:
|
750 |
+
hidden_states = hidden_states + residual
|
751 |
+
|
752 |
+
hidden_states = hidden_states / attn.rescale_output_factor
|
753 |
+
|
754 |
+
return hidden_states
|
ip_adapter/ip_adapter.py
ADDED
@@ -0,0 +1,1078 @@
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|
1 |
+
import os
|
2 |
+
from typing import List
|
3 |
+
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4 |
+
import torch
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5 |
+
from diffusers import StableDiffusionPipeline
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6 |
+
from diffusers.pipelines.controlnet import MultiControlNetModel
|
7 |
+
from PIL import Image
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8 |
+
from safetensors import safe_open
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9 |
+
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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10 |
+
from torchvision import transforms
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11 |
+
from .utils import is_torch2_available, get_generator
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12 |
+
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+
# import torchvision.transforms.functional as Func
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14 |
+
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15 |
+
# from .clip_style_models import CSD_CLIP, convert_state_dict
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+
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17 |
+
if is_torch2_available():
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18 |
+
from .attention_processor import (
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19 |
+
AttnProcessor2_0 as AttnProcessor,
|
20 |
+
)
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21 |
+
from .attention_processor import (
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22 |
+
CNAttnProcessor2_0 as CNAttnProcessor,
|
23 |
+
)
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24 |
+
from .attention_processor import (
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25 |
+
IPAttnProcessor2_0 as IPAttnProcessor,
|
26 |
+
)
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27 |
+
from .attention_processor import IP_CS_AttnProcessor2_0 as IP_CS_AttnProcessor
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28 |
+
else:
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29 |
+
from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
|
30 |
+
from .resampler import Resampler
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31 |
+
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+
from transformers import AutoImageProcessor, AutoModel
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+
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+
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+
class ImageProjModel(torch.nn.Module):
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36 |
+
"""Projection Model"""
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37 |
+
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38 |
+
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
|
39 |
+
super().__init__()
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40 |
+
|
41 |
+
self.generator = None
|
42 |
+
self.cross_attention_dim = cross_attention_dim
|
43 |
+
self.clip_extra_context_tokens = clip_extra_context_tokens
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44 |
+
# print(clip_embeddings_dim, self.clip_extra_context_tokens, cross_attention_dim)
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45 |
+
self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
|
46 |
+
self.norm = torch.nn.LayerNorm(cross_attention_dim)
|
47 |
+
|
48 |
+
def forward(self, image_embeds):
|
49 |
+
embeds = image_embeds
|
50 |
+
clip_extra_context_tokens = self.proj(embeds).reshape(
|
51 |
+
-1, self.clip_extra_context_tokens, self.cross_attention_dim
|
52 |
+
)
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53 |
+
clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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54 |
+
return clip_extra_context_tokens
|
55 |
+
|
56 |
+
|
57 |
+
class MLPProjModel(torch.nn.Module):
|
58 |
+
"""SD model with image prompt"""
|
59 |
+
|
60 |
+
def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
|
61 |
+
super().__init__()
|
62 |
+
|
63 |
+
self.proj = torch.nn.Sequential(
|
64 |
+
torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
|
65 |
+
torch.nn.GELU(),
|
66 |
+
torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
|
67 |
+
torch.nn.LayerNorm(cross_attention_dim)
|
68 |
+
)
|
69 |
+
|
70 |
+
def forward(self, image_embeds):
|
71 |
+
clip_extra_context_tokens = self.proj(image_embeds)
|
72 |
+
return clip_extra_context_tokens
|
73 |
+
|
74 |
+
|
75 |
+
class IPAdapter:
|
76 |
+
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4, target_blocks=["block"]):
|
77 |
+
self.device = device
|
78 |
+
self.image_encoder_path = image_encoder_path
|
79 |
+
self.ip_ckpt = ip_ckpt
|
80 |
+
self.num_tokens = num_tokens
|
81 |
+
self.target_blocks = target_blocks
|
82 |
+
|
83 |
+
self.pipe = sd_pipe.to(self.device)
|
84 |
+
self.set_ip_adapter()
|
85 |
+
|
86 |
+
# load image encoder
|
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+
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
88 |
+
self.device, dtype=torch.float16
|
89 |
+
)
|
90 |
+
self.clip_image_processor = CLIPImageProcessor()
|
91 |
+
# image proj model
|
92 |
+
self.image_proj_model = self.init_proj()
|
93 |
+
|
94 |
+
self.load_ip_adapter()
|
95 |
+
|
96 |
+
def init_proj(self):
|
97 |
+
image_proj_model = ImageProjModel(
|
98 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
99 |
+
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
100 |
+
clip_extra_context_tokens=self.num_tokens,
|
101 |
+
).to(self.device, dtype=torch.float16)
|
102 |
+
return image_proj_model
|
103 |
+
|
104 |
+
def set_ip_adapter(self):
|
105 |
+
unet = self.pipe.unet
|
106 |
+
attn_procs = {}
|
107 |
+
for name in unet.attn_processors.keys():
|
108 |
+
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
|
109 |
+
if name.startswith("mid_block"):
|
110 |
+
hidden_size = unet.config.block_out_channels[-1]
|
111 |
+
elif name.startswith("up_blocks"):
|
112 |
+
block_id = int(name[len("up_blocks.")])
|
113 |
+
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
|
114 |
+
elif name.startswith("down_blocks"):
|
115 |
+
block_id = int(name[len("down_blocks.")])
|
116 |
+
hidden_size = unet.config.block_out_channels[block_id]
|
117 |
+
if cross_attention_dim is None:
|
118 |
+
attn_procs[name] = AttnProcessor()
|
119 |
+
else:
|
120 |
+
selected = False
|
121 |
+
for block_name in self.target_blocks:
|
122 |
+
if block_name in name:
|
123 |
+
selected = True
|
124 |
+
break
|
125 |
+
if selected:
|
126 |
+
attn_procs[name] = IPAttnProcessor(
|
127 |
+
hidden_size=hidden_size,
|
128 |
+
cross_attention_dim=cross_attention_dim,
|
129 |
+
scale=1.0,
|
130 |
+
num_tokens=self.num_tokens,
|
131 |
+
).to(self.device, dtype=torch.float16)
|
132 |
+
else:
|
133 |
+
attn_procs[name] = IPAttnProcessor(
|
134 |
+
hidden_size=hidden_size,
|
135 |
+
cross_attention_dim=cross_attention_dim,
|
136 |
+
scale=1.0,
|
137 |
+
num_tokens=self.num_tokens,
|
138 |
+
skip=True
|
139 |
+
).to(self.device, dtype=torch.float16)
|
140 |
+
unet.set_attn_processor(attn_procs)
|
141 |
+
if hasattr(self.pipe, "controlnet"):
|
142 |
+
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
143 |
+
for controlnet in self.pipe.controlnet.nets:
|
144 |
+
controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
145 |
+
else:
|
146 |
+
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
|
147 |
+
|
148 |
+
def load_ip_adapter(self):
|
149 |
+
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
150 |
+
state_dict = {"image_proj": {}, "ip_adapter": {}}
|
151 |
+
with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
|
152 |
+
for key in f.keys():
|
153 |
+
if key.startswith("image_proj."):
|
154 |
+
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
|
155 |
+
elif key.startswith("ip_adapter."):
|
156 |
+
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
|
157 |
+
else:
|
158 |
+
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
|
159 |
+
self.image_proj_model.load_state_dict(state_dict["image_proj"])
|
160 |
+
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
161 |
+
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
162 |
+
|
163 |
+
@torch.inference_mode()
|
164 |
+
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None):
|
165 |
+
if pil_image is not None:
|
166 |
+
if isinstance(pil_image, Image.Image):
|
167 |
+
pil_image = [pil_image]
|
168 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
169 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
170 |
+
else:
|
171 |
+
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
172 |
+
|
173 |
+
if content_prompt_embeds is not None:
|
174 |
+
clip_image_embeds = clip_image_embeds - content_prompt_embeds
|
175 |
+
|
176 |
+
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
177 |
+
uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
|
178 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
179 |
+
|
180 |
+
def set_scale(self, scale):
|
181 |
+
for attn_processor in self.pipe.unet.attn_processors.values():
|
182 |
+
if isinstance(attn_processor, IPAttnProcessor):
|
183 |
+
attn_processor.scale = scale
|
184 |
+
|
185 |
+
def generate(
|
186 |
+
self,
|
187 |
+
pil_image=None,
|
188 |
+
clip_image_embeds=None,
|
189 |
+
prompt=None,
|
190 |
+
negative_prompt=None,
|
191 |
+
scale=1.0,
|
192 |
+
num_samples=4,
|
193 |
+
seed=None,
|
194 |
+
guidance_scale=7.5,
|
195 |
+
num_inference_steps=30,
|
196 |
+
neg_content_emb=None,
|
197 |
+
**kwargs,
|
198 |
+
):
|
199 |
+
self.set_scale(scale)
|
200 |
+
|
201 |
+
if pil_image is not None:
|
202 |
+
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
203 |
+
else:
|
204 |
+
num_prompts = clip_image_embeds.size(0)
|
205 |
+
|
206 |
+
if prompt is None:
|
207 |
+
prompt = "best quality, high quality"
|
208 |
+
if negative_prompt is None:
|
209 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
210 |
+
|
211 |
+
if not isinstance(prompt, List):
|
212 |
+
prompt = [prompt] * num_prompts
|
213 |
+
if not isinstance(negative_prompt, List):
|
214 |
+
negative_prompt = [negative_prompt] * num_prompts
|
215 |
+
|
216 |
+
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
|
217 |
+
pil_image=pil_image, clip_image_embeds=clip_image_embeds, content_prompt_embeds=neg_content_emb
|
218 |
+
)
|
219 |
+
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
220 |
+
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
221 |
+
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
222 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
223 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
224 |
+
|
225 |
+
with torch.inference_mode():
|
226 |
+
prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
|
227 |
+
prompt,
|
228 |
+
device=self.device,
|
229 |
+
num_images_per_prompt=num_samples,
|
230 |
+
do_classifier_free_guidance=True,
|
231 |
+
negative_prompt=negative_prompt,
|
232 |
+
)
|
233 |
+
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
|
234 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
|
235 |
+
|
236 |
+
generator = get_generator(seed, self.device)
|
237 |
+
|
238 |
+
images = self.pipe(
|
239 |
+
prompt_embeds=prompt_embeds,
|
240 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
241 |
+
guidance_scale=guidance_scale,
|
242 |
+
num_inference_steps=num_inference_steps,
|
243 |
+
generator=generator,
|
244 |
+
**kwargs,
|
245 |
+
).images
|
246 |
+
|
247 |
+
return images
|
248 |
+
|
249 |
+
|
250 |
+
class IPAdapter_CS:
|
251 |
+
def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_content_tokens=4,
|
252 |
+
num_style_tokens=4,
|
253 |
+
target_content_blocks=["block"], target_style_blocks=["block"], content_image_encoder_path=None,
|
254 |
+
controlnet_adapter=False,
|
255 |
+
controlnet_target_content_blocks=None,
|
256 |
+
controlnet_target_style_blocks=None,
|
257 |
+
content_model_resampler=False,
|
258 |
+
style_model_resampler=False,
|
259 |
+
):
|
260 |
+
self.device = device
|
261 |
+
self.image_encoder_path = image_encoder_path
|
262 |
+
self.ip_ckpt = ip_ckpt
|
263 |
+
self.num_content_tokens = num_content_tokens
|
264 |
+
self.num_style_tokens = num_style_tokens
|
265 |
+
self.content_target_blocks = target_content_blocks
|
266 |
+
self.style_target_blocks = target_style_blocks
|
267 |
+
|
268 |
+
self.content_model_resampler = content_model_resampler
|
269 |
+
self.style_model_resampler = style_model_resampler
|
270 |
+
|
271 |
+
self.controlnet_adapter = controlnet_adapter
|
272 |
+
self.controlnet_target_content_blocks = controlnet_target_content_blocks
|
273 |
+
self.controlnet_target_style_blocks = controlnet_target_style_blocks
|
274 |
+
|
275 |
+
self.pipe = sd_pipe.to(self.device)
|
276 |
+
self.set_ip_adapter()
|
277 |
+
self.content_image_encoder_path = content_image_encoder_path
|
278 |
+
|
279 |
+
|
280 |
+
# load image encoder
|
281 |
+
if content_image_encoder_path is not None:
|
282 |
+
self.content_image_encoder = AutoModel.from_pretrained(content_image_encoder_path).to(self.device,
|
283 |
+
dtype=torch.float16)
|
284 |
+
self.content_image_processor = AutoImageProcessor.from_pretrained(content_image_encoder_path)
|
285 |
+
else:
|
286 |
+
self.content_image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
287 |
+
self.device, dtype=torch.float16
|
288 |
+
)
|
289 |
+
self.content_image_processor = CLIPImageProcessor()
|
290 |
+
# model.requires_grad_(False)
|
291 |
+
|
292 |
+
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
|
293 |
+
self.device, dtype=torch.float16
|
294 |
+
)
|
295 |
+
# if self.use_CSD is not None:
|
296 |
+
# self.style_image_encoder = CSD_CLIP("vit_large", "default",self.use_CSD+"/ViT-L-14.pt")
|
297 |
+
# model_path = self.use_CSD+"/checkpoint.pth"
|
298 |
+
# checkpoint = torch.load(model_path, map_location="cpu")
|
299 |
+
# state_dict = convert_state_dict(checkpoint['model_state_dict'])
|
300 |
+
# self.style_image_encoder.load_state_dict(state_dict, strict=False)
|
301 |
+
#
|
302 |
+
# normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
|
303 |
+
# self.style_preprocess = transforms.Compose([
|
304 |
+
# transforms.Resize(size=224, interpolation=Func.InterpolationMode.BICUBIC),
|
305 |
+
# transforms.CenterCrop(224),
|
306 |
+
# transforms.ToTensor(),
|
307 |
+
# normalize,
|
308 |
+
# ])
|
309 |
+
|
310 |
+
self.clip_image_processor = CLIPImageProcessor()
|
311 |
+
# image proj model
|
312 |
+
self.content_image_proj_model = self.init_proj(self.num_content_tokens, content_or_style_='content',
|
313 |
+
model_resampler=self.content_model_resampler)
|
314 |
+
self.style_image_proj_model = self.init_proj(self.num_style_tokens, content_or_style_='style',
|
315 |
+
model_resampler=self.style_model_resampler)
|
316 |
+
|
317 |
+
self.load_ip_adapter()
|
318 |
+
|
319 |
+
def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
|
320 |
+
|
321 |
+
# print('@@@@',self.pipe.unet.config.cross_attention_dim,self.image_encoder.config.projection_dim)
|
322 |
+
if content_or_style_ == 'content' and self.content_image_encoder_path is not None:
|
323 |
+
image_proj_model = ImageProjModel(
|
324 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
325 |
+
clip_embeddings_dim=self.content_image_encoder.config.projection_dim,
|
326 |
+
clip_extra_context_tokens=num_tokens,
|
327 |
+
).to(self.device, dtype=torch.float16)
|
328 |
+
return image_proj_model
|
329 |
+
|
330 |
+
image_proj_model = ImageProjModel(
|
331 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
332 |
+
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
333 |
+
clip_extra_context_tokens=num_tokens,
|
334 |
+
).to(self.device, dtype=torch.float16)
|
335 |
+
return image_proj_model
|
336 |
+
|
337 |
+
def set_ip_adapter(self):
|
338 |
+
unet = self.pipe.unet
|
339 |
+
attn_procs = {}
|
340 |
+
for name in unet.attn_processors.keys():
|
341 |
+
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
|
342 |
+
if name.startswith("mid_block"):
|
343 |
+
hidden_size = unet.config.block_out_channels[-1]
|
344 |
+
elif name.startswith("up_blocks"):
|
345 |
+
block_id = int(name[len("up_blocks.")])
|
346 |
+
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
|
347 |
+
elif name.startswith("down_blocks"):
|
348 |
+
block_id = int(name[len("down_blocks.")])
|
349 |
+
hidden_size = unet.config.block_out_channels[block_id]
|
350 |
+
if cross_attention_dim is None:
|
351 |
+
attn_procs[name] = AttnProcessor()
|
352 |
+
else:
|
353 |
+
# layername_id += 1
|
354 |
+
selected = False
|
355 |
+
for block_name in self.style_target_blocks:
|
356 |
+
if block_name in name:
|
357 |
+
selected = True
|
358 |
+
# print(name)
|
359 |
+
attn_procs[name] = IP_CS_AttnProcessor(
|
360 |
+
hidden_size=hidden_size,
|
361 |
+
cross_attention_dim=cross_attention_dim,
|
362 |
+
style_scale=1.0,
|
363 |
+
style=True,
|
364 |
+
num_content_tokens=self.num_content_tokens,
|
365 |
+
num_style_tokens=self.num_style_tokens,
|
366 |
+
)
|
367 |
+
for block_name in self.content_target_blocks:
|
368 |
+
if block_name in name:
|
369 |
+
# selected = True
|
370 |
+
if selected is False:
|
371 |
+
attn_procs[name] = IP_CS_AttnProcessor(
|
372 |
+
hidden_size=hidden_size,
|
373 |
+
cross_attention_dim=cross_attention_dim,
|
374 |
+
content_scale=1.0,
|
375 |
+
content=True,
|
376 |
+
num_content_tokens=self.num_content_tokens,
|
377 |
+
num_style_tokens=self.num_style_tokens,
|
378 |
+
)
|
379 |
+
else:
|
380 |
+
attn_procs[name].set_content_ipa(content_scale=1.0)
|
381 |
+
# attn_procs[name].content=True
|
382 |
+
|
383 |
+
if selected is False:
|
384 |
+
attn_procs[name] = IP_CS_AttnProcessor(
|
385 |
+
hidden_size=hidden_size,
|
386 |
+
cross_attention_dim=cross_attention_dim,
|
387 |
+
num_content_tokens=self.num_content_tokens,
|
388 |
+
num_style_tokens=self.num_style_tokens,
|
389 |
+
skip=True,
|
390 |
+
)
|
391 |
+
|
392 |
+
attn_procs[name].to(self.device, dtype=torch.float16)
|
393 |
+
unet.set_attn_processor(attn_procs)
|
394 |
+
if hasattr(self.pipe, "controlnet"):
|
395 |
+
if self.controlnet_adapter is False:
|
396 |
+
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
397 |
+
for controlnet in self.pipe.controlnet.nets:
|
398 |
+
controlnet.set_attn_processor(CNAttnProcessor(
|
399 |
+
num_tokens=self.num_content_tokens + self.num_style_tokens))
|
400 |
+
else:
|
401 |
+
self.pipe.controlnet.set_attn_processor(CNAttnProcessor(
|
402 |
+
num_tokens=self.num_content_tokens + self.num_style_tokens))
|
403 |
+
|
404 |
+
else:
|
405 |
+
controlnet_attn_procs = {}
|
406 |
+
controlnet_style_target_blocks = self.controlnet_target_style_blocks
|
407 |
+
controlnet_content_target_blocks = self.controlnet_target_content_blocks
|
408 |
+
for name in self.pipe.controlnet.attn_processors.keys():
|
409 |
+
# print(name)
|
410 |
+
cross_attention_dim = None if name.endswith(
|
411 |
+
"attn1.processor") else self.pipe.controlnet.config.cross_attention_dim
|
412 |
+
if name.startswith("mid_block"):
|
413 |
+
hidden_size = self.pipe.controlnet.config.block_out_channels[-1]
|
414 |
+
elif name.startswith("up_blocks"):
|
415 |
+
block_id = int(name[len("up_blocks.")])
|
416 |
+
hidden_size = list(reversed(self.pipe.controlnet.config.block_out_channels))[block_id]
|
417 |
+
elif name.startswith("down_blocks"):
|
418 |
+
block_id = int(name[len("down_blocks.")])
|
419 |
+
hidden_size = self.pipe.controlnet.config.block_out_channels[block_id]
|
420 |
+
if cross_attention_dim is None:
|
421 |
+
# layername_id += 1
|
422 |
+
controlnet_attn_procs[name] = AttnProcessor()
|
423 |
+
|
424 |
+
else:
|
425 |
+
# layername_id += 1
|
426 |
+
selected = False
|
427 |
+
for block_name in controlnet_style_target_blocks:
|
428 |
+
if block_name in name:
|
429 |
+
selected = True
|
430 |
+
# print(name)
|
431 |
+
controlnet_attn_procs[name] = IP_CS_AttnProcessor(
|
432 |
+
hidden_size=hidden_size,
|
433 |
+
cross_attention_dim=cross_attention_dim,
|
434 |
+
style_scale=1.0,
|
435 |
+
style=True,
|
436 |
+
num_content_tokens=self.num_content_tokens,
|
437 |
+
num_style_tokens=self.num_style_tokens,
|
438 |
+
)
|
439 |
+
|
440 |
+
for block_name in controlnet_content_target_blocks:
|
441 |
+
if block_name in name:
|
442 |
+
if selected is False:
|
443 |
+
controlnet_attn_procs[name] = IP_CS_AttnProcessor(
|
444 |
+
hidden_size=hidden_size,
|
445 |
+
cross_attention_dim=cross_attention_dim,
|
446 |
+
content_scale=1.0,
|
447 |
+
content=True,
|
448 |
+
num_content_tokens=self.num_content_tokens,
|
449 |
+
num_style_tokens=self.num_style_tokens,
|
450 |
+
)
|
451 |
+
|
452 |
+
selected = True
|
453 |
+
elif selected is True:
|
454 |
+
controlnet_attn_procs[name].set_content_ipa(content_scale=1.0)
|
455 |
+
|
456 |
+
# if args.content_image_encoder_type !='dinov2':
|
457 |
+
# weights = {
|
458 |
+
# "to_k_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_k_ip.weight"],
|
459 |
+
# "to_v_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_v_ip.weight"],
|
460 |
+
# }
|
461 |
+
# attn_procs[name].load_state_dict(weights)
|
462 |
+
if selected is False:
|
463 |
+
controlnet_attn_procs[name] = IP_CS_AttnProcessor(
|
464 |
+
hidden_size=hidden_size,
|
465 |
+
cross_attention_dim=cross_attention_dim,
|
466 |
+
num_content_tokens=self.num_content_tokens,
|
467 |
+
num_style_tokens=self.num_style_tokens,
|
468 |
+
skip=True,
|
469 |
+
)
|
470 |
+
controlnet_attn_procs[name].to(self.device, dtype=torch.float16)
|
471 |
+
# layer_name = name.split(".processor")[0]
|
472 |
+
# # print(state_dict["ip_adapter"].keys())
|
473 |
+
# weights = {
|
474 |
+
# "to_k_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_k_ip.weight"],
|
475 |
+
# "to_v_ip.weight": state_dict["ip_adapter"][str(layername_id) + ".to_v_ip.weight"],
|
476 |
+
# }
|
477 |
+
# attn_procs[name].load_state_dict(weights)
|
478 |
+
self.pipe.controlnet.set_attn_processor(controlnet_attn_procs)
|
479 |
+
|
480 |
+
def load_ip_adapter(self):
|
481 |
+
if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
|
482 |
+
state_dict = {"content_image_proj": {}, "style_image_proj": {}, "ip_adapter": {}}
|
483 |
+
with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
|
484 |
+
for key in f.keys():
|
485 |
+
if key.startswith("content_image_proj."):
|
486 |
+
state_dict["content_image_proj"][key.replace("content_image_proj.", "")] = f.get_tensor(key)
|
487 |
+
elif key.startswith("style_image_proj."):
|
488 |
+
state_dict["style_image_proj"][key.replace("style_image_proj.", "")] = f.get_tensor(key)
|
489 |
+
elif key.startswith("ip_adapter."):
|
490 |
+
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
|
491 |
+
else:
|
492 |
+
state_dict = torch.load(self.ip_ckpt, map_location="cpu")
|
493 |
+
self.content_image_proj_model.load_state_dict(state_dict["content_image_proj"])
|
494 |
+
self.style_image_proj_model.load_state_dict(state_dict["style_image_proj"])
|
495 |
+
|
496 |
+
if 'conv_in_unet_sd' in state_dict.keys():
|
497 |
+
self.pipe.unet.conv_in.load_state_dict(state_dict["conv_in_unet_sd"], strict=True)
|
498 |
+
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
499 |
+
ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
|
500 |
+
|
501 |
+
if self.controlnet_adapter is True:
|
502 |
+
print('loading controlnet_adapter')
|
503 |
+
self.pipe.controlnet.load_state_dict(state_dict["controlnet_adapter_modules"], strict=False)
|
504 |
+
|
505 |
+
@torch.inference_mode()
|
506 |
+
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_prompt_embeds=None,
|
507 |
+
content_or_style_=''):
|
508 |
+
# if pil_image is not None:
|
509 |
+
# if isinstance(pil_image, Image.Image):
|
510 |
+
# pil_image = [pil_image]
|
511 |
+
# clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
512 |
+
# clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
513 |
+
# else:
|
514 |
+
# clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
515 |
+
|
516 |
+
# if content_prompt_embeds is not None:
|
517 |
+
# clip_image_embeds = clip_image_embeds - content_prompt_embeds
|
518 |
+
|
519 |
+
if content_or_style_ == 'content':
|
520 |
+
if pil_image is not None:
|
521 |
+
if isinstance(pil_image, Image.Image):
|
522 |
+
pil_image = [pil_image]
|
523 |
+
if self.content_image_proj_model is not None:
|
524 |
+
clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
525 |
+
clip_image_embeds = self.content_image_encoder(
|
526 |
+
clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
527 |
+
else:
|
528 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
529 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
530 |
+
else:
|
531 |
+
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
532 |
+
|
533 |
+
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
534 |
+
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
535 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
536 |
+
if content_or_style_ == 'style':
|
537 |
+
if pil_image is not None:
|
538 |
+
if self.use_CSD is not None:
|
539 |
+
clip_image = self.style_preprocess(pil_image).unsqueeze(0).to(self.device, dtype=torch.float32)
|
540 |
+
clip_image_embeds = self.style_image_encoder(clip_image)
|
541 |
+
else:
|
542 |
+
if isinstance(pil_image, Image.Image):
|
543 |
+
pil_image = [pil_image]
|
544 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
545 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
546 |
+
|
547 |
+
|
548 |
+
else:
|
549 |
+
clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
550 |
+
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
551 |
+
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
552 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
553 |
+
|
554 |
+
def set_scale(self, content_scale, style_scale):
|
555 |
+
for attn_processor in self.pipe.unet.attn_processors.values():
|
556 |
+
if isinstance(attn_processor, IP_CS_AttnProcessor):
|
557 |
+
if attn_processor.content is True:
|
558 |
+
attn_processor.content_scale = content_scale
|
559 |
+
|
560 |
+
if attn_processor.style is True:
|
561 |
+
attn_processor.style_scale = style_scale
|
562 |
+
# print('style_scale:',style_scale)
|
563 |
+
if self.controlnet_adapter is not None:
|
564 |
+
for attn_processor in self.pipe.controlnet.attn_processors.values():
|
565 |
+
|
566 |
+
if isinstance(attn_processor, IP_CS_AttnProcessor):
|
567 |
+
if attn_processor.content is True:
|
568 |
+
attn_processor.content_scale = content_scale
|
569 |
+
# print(content_scale)
|
570 |
+
|
571 |
+
if attn_processor.style is True:
|
572 |
+
attn_processor.style_scale = style_scale
|
573 |
+
|
574 |
+
def generate(
|
575 |
+
self,
|
576 |
+
pil_content_image=None,
|
577 |
+
pil_style_image=None,
|
578 |
+
clip_content_image_embeds=None,
|
579 |
+
clip_style_image_embeds=None,
|
580 |
+
prompt=None,
|
581 |
+
negative_prompt=None,
|
582 |
+
content_scale=1.0,
|
583 |
+
style_scale=1.0,
|
584 |
+
num_samples=4,
|
585 |
+
seed=None,
|
586 |
+
guidance_scale=7.5,
|
587 |
+
num_inference_steps=30,
|
588 |
+
neg_content_emb=None,
|
589 |
+
**kwargs,
|
590 |
+
):
|
591 |
+
self.set_scale(content_scale, style_scale)
|
592 |
+
|
593 |
+
if pil_content_image is not None:
|
594 |
+
num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image)
|
595 |
+
else:
|
596 |
+
num_prompts = clip_content_image_embeds.size(0)
|
597 |
+
|
598 |
+
if prompt is None:
|
599 |
+
prompt = "best quality, high quality"
|
600 |
+
if negative_prompt is None:
|
601 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
602 |
+
|
603 |
+
if not isinstance(prompt, List):
|
604 |
+
prompt = [prompt] * num_prompts
|
605 |
+
if not isinstance(negative_prompt, List):
|
606 |
+
negative_prompt = [negative_prompt] * num_prompts
|
607 |
+
|
608 |
+
content_image_prompt_embeds, uncond_content_image_prompt_embeds = self.get_image_embeds(
|
609 |
+
pil_image=pil_content_image, clip_image_embeds=clip_content_image_embeds
|
610 |
+
)
|
611 |
+
style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(
|
612 |
+
pil_image=pil_style_image, clip_image_embeds=clip_style_image_embeds
|
613 |
+
)
|
614 |
+
|
615 |
+
bs_embed, seq_len, _ = content_image_prompt_embeds.shape
|
616 |
+
content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1)
|
617 |
+
content_image_prompt_embeds = content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
618 |
+
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.repeat(1, num_samples, 1)
|
619 |
+
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.view(bs_embed * num_samples, seq_len,
|
620 |
+
-1)
|
621 |
+
|
622 |
+
bs_style_embed, seq_style_len, _ = content_image_prompt_embeds.shape
|
623 |
+
style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1)
|
624 |
+
style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1)
|
625 |
+
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1)
|
626 |
+
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len,
|
627 |
+
-1)
|
628 |
+
|
629 |
+
with torch.inference_mode():
|
630 |
+
prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
|
631 |
+
prompt,
|
632 |
+
device=self.device,
|
633 |
+
num_images_per_prompt=num_samples,
|
634 |
+
do_classifier_free_guidance=True,
|
635 |
+
negative_prompt=negative_prompt,
|
636 |
+
)
|
637 |
+
prompt_embeds = torch.cat([prompt_embeds_, content_image_prompt_embeds, style_image_prompt_embeds], dim=1)
|
638 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds_,
|
639 |
+
uncond_content_image_prompt_embeds, uncond_style_image_prompt_embeds],
|
640 |
+
dim=1)
|
641 |
+
|
642 |
+
generator = get_generator(seed, self.device)
|
643 |
+
|
644 |
+
images = self.pipe(
|
645 |
+
prompt_embeds=prompt_embeds,
|
646 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
647 |
+
guidance_scale=guidance_scale,
|
648 |
+
num_inference_steps=num_inference_steps,
|
649 |
+
generator=generator,
|
650 |
+
**kwargs,
|
651 |
+
).images
|
652 |
+
|
653 |
+
return images
|
654 |
+
|
655 |
+
|
656 |
+
class IPAdapterXL_CS(IPAdapter_CS):
|
657 |
+
"""SDXL"""
|
658 |
+
|
659 |
+
def generate(
|
660 |
+
self,
|
661 |
+
pil_content_image,
|
662 |
+
pil_style_image,
|
663 |
+
prompt=None,
|
664 |
+
negative_prompt=None,
|
665 |
+
content_scale=1.0,
|
666 |
+
style_scale=1.0,
|
667 |
+
num_samples=4,
|
668 |
+
seed=None,
|
669 |
+
content_image_embeds=None,
|
670 |
+
style_image_embeds=None,
|
671 |
+
num_inference_steps=30,
|
672 |
+
neg_content_emb=None,
|
673 |
+
neg_content_prompt=None,
|
674 |
+
neg_content_scale=1.0,
|
675 |
+
**kwargs,
|
676 |
+
):
|
677 |
+
self.set_scale(content_scale, style_scale)
|
678 |
+
|
679 |
+
num_prompts = 1 if isinstance(pil_content_image, Image.Image) else len(pil_content_image)
|
680 |
+
|
681 |
+
if prompt is None:
|
682 |
+
prompt = "best quality, high quality"
|
683 |
+
if negative_prompt is None:
|
684 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
685 |
+
|
686 |
+
if not isinstance(prompt, List):
|
687 |
+
prompt = [prompt] * num_prompts
|
688 |
+
if not isinstance(negative_prompt, List):
|
689 |
+
negative_prompt = [negative_prompt] * num_prompts
|
690 |
+
|
691 |
+
content_image_prompt_embeds, uncond_content_image_prompt_embeds = self.get_image_embeds(pil_content_image,
|
692 |
+
content_image_embeds,
|
693 |
+
content_or_style_='content')
|
694 |
+
|
695 |
+
|
696 |
+
|
697 |
+
style_image_prompt_embeds, uncond_style_image_prompt_embeds = self.get_image_embeds(pil_style_image,
|
698 |
+
style_image_embeds,
|
699 |
+
content_or_style_='style')
|
700 |
+
|
701 |
+
bs_embed, seq_len, _ = content_image_prompt_embeds.shape
|
702 |
+
|
703 |
+
content_image_prompt_embeds = content_image_prompt_embeds.repeat(1, num_samples, 1)
|
704 |
+
content_image_prompt_embeds = content_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
705 |
+
|
706 |
+
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.repeat(1, num_samples, 1)
|
707 |
+
uncond_content_image_prompt_embeds = uncond_content_image_prompt_embeds.view(bs_embed * num_samples, seq_len,
|
708 |
+
-1)
|
709 |
+
bs_style_embed, seq_style_len, _ = style_image_prompt_embeds.shape
|
710 |
+
style_image_prompt_embeds = style_image_prompt_embeds.repeat(1, num_samples, 1)
|
711 |
+
style_image_prompt_embeds = style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len, -1)
|
712 |
+
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.repeat(1, num_samples, 1)
|
713 |
+
uncond_style_image_prompt_embeds = uncond_style_image_prompt_embeds.view(bs_embed * num_samples, seq_style_len,
|
714 |
+
-1)
|
715 |
+
|
716 |
+
with torch.inference_mode():
|
717 |
+
(
|
718 |
+
prompt_embeds,
|
719 |
+
negative_prompt_embeds,
|
720 |
+
pooled_prompt_embeds,
|
721 |
+
negative_pooled_prompt_embeds,
|
722 |
+
) = self.pipe.encode_prompt(
|
723 |
+
prompt,
|
724 |
+
num_images_per_prompt=num_samples,
|
725 |
+
do_classifier_free_guidance=True,
|
726 |
+
negative_prompt=negative_prompt,
|
727 |
+
)
|
728 |
+
prompt_embeds = torch.cat([prompt_embeds, content_image_prompt_embeds, style_image_prompt_embeds], dim=1)
|
729 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds,
|
730 |
+
uncond_content_image_prompt_embeds, uncond_style_image_prompt_embeds],
|
731 |
+
dim=1)
|
732 |
+
|
733 |
+
self.generator = get_generator(seed, self.device)
|
734 |
+
|
735 |
+
images = self.pipe(
|
736 |
+
prompt_embeds=prompt_embeds,
|
737 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
738 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
739 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
740 |
+
num_inference_steps=num_inference_steps,
|
741 |
+
generator=self.generator,
|
742 |
+
**kwargs,
|
743 |
+
).images
|
744 |
+
return images
|
745 |
+
|
746 |
+
|
747 |
+
class CSGO(IPAdapterXL_CS):
|
748 |
+
"""SDXL"""
|
749 |
+
|
750 |
+
def init_proj(self, num_tokens, content_or_style_='content', model_resampler=False):
|
751 |
+
if content_or_style_ == 'content':
|
752 |
+
if model_resampler:
|
753 |
+
image_proj_model = Resampler(
|
754 |
+
dim=self.pipe.unet.config.cross_attention_dim,
|
755 |
+
depth=4,
|
756 |
+
dim_head=64,
|
757 |
+
heads=12,
|
758 |
+
num_queries=num_tokens,
|
759 |
+
embedding_dim=self.content_image_encoder.config.hidden_size,
|
760 |
+
output_dim=self.pipe.unet.config.cross_attention_dim,
|
761 |
+
ff_mult=4,
|
762 |
+
).to(self.device, dtype=torch.float16)
|
763 |
+
else:
|
764 |
+
image_proj_model = ImageProjModel(
|
765 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
766 |
+
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
767 |
+
clip_extra_context_tokens=num_tokens,
|
768 |
+
).to(self.device, dtype=torch.float16)
|
769 |
+
if content_or_style_ == 'style':
|
770 |
+
if model_resampler:
|
771 |
+
image_proj_model = Resampler(
|
772 |
+
dim=self.pipe.unet.config.cross_attention_dim,
|
773 |
+
depth=4,
|
774 |
+
dim_head=64,
|
775 |
+
heads=12,
|
776 |
+
num_queries=num_tokens,
|
777 |
+
embedding_dim=self.content_image_encoder.config.hidden_size,
|
778 |
+
output_dim=self.pipe.unet.config.cross_attention_dim,
|
779 |
+
ff_mult=4,
|
780 |
+
).to(self.device, dtype=torch.float16)
|
781 |
+
else:
|
782 |
+
image_proj_model = ImageProjModel(
|
783 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
784 |
+
clip_embeddings_dim=self.image_encoder.config.projection_dim,
|
785 |
+
clip_extra_context_tokens=num_tokens,
|
786 |
+
).to(self.device, dtype=torch.float16)
|
787 |
+
return image_proj_model
|
788 |
+
|
789 |
+
@torch.inference_mode()
|
790 |
+
def get_image_embeds(self, pil_image=None, clip_image_embeds=None, content_or_style_=''):
|
791 |
+
if isinstance(pil_image, Image.Image):
|
792 |
+
pil_image = [pil_image]
|
793 |
+
if content_or_style_ == 'style':
|
794 |
+
|
795 |
+
if self.style_model_resampler:
|
796 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
797 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16),
|
798 |
+
output_hidden_states=True).hidden_states[-2]
|
799 |
+
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
800 |
+
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
801 |
+
else:
|
802 |
+
|
803 |
+
|
804 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
805 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
806 |
+
image_prompt_embeds = self.style_image_proj_model(clip_image_embeds)
|
807 |
+
uncond_image_prompt_embeds = self.style_image_proj_model(torch.zeros_like(clip_image_embeds))
|
808 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
809 |
+
|
810 |
+
|
811 |
+
else:
|
812 |
+
|
813 |
+
if self.content_image_encoder_path is not None:
|
814 |
+
clip_image = self.content_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
815 |
+
outputs = self.content_image_encoder(clip_image.to(self.device, dtype=torch.float16),
|
816 |
+
output_hidden_states=True)
|
817 |
+
clip_image_embeds = outputs.last_hidden_state
|
818 |
+
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
819 |
+
|
820 |
+
# uncond_clip_image_embeds = self.image_encoder(
|
821 |
+
# torch.zeros_like(clip_image), output_hidden_states=True
|
822 |
+
# ).last_hidden_state
|
823 |
+
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
824 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
825 |
+
|
826 |
+
else:
|
827 |
+
if self.content_model_resampler:
|
828 |
+
|
829 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
830 |
+
|
831 |
+
clip_image = clip_image.to(self.device, dtype=torch.float16)
|
832 |
+
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
833 |
+
# clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
|
834 |
+
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
835 |
+
# uncond_clip_image_embeds = self.image_encoder(
|
836 |
+
# torch.zeros_like(clip_image), output_hidden_states=True
|
837 |
+
# ).hidden_states[-2]
|
838 |
+
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
839 |
+
else:
|
840 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
841 |
+
clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
|
842 |
+
image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
843 |
+
uncond_image_prompt_embeds = self.content_image_proj_model(torch.zeros_like(clip_image_embeds))
|
844 |
+
|
845 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
846 |
+
|
847 |
+
# # clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
848 |
+
# clip_image = clip_image.to(self.device, dtype=torch.float16)
|
849 |
+
# clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
850 |
+
# image_prompt_embeds = self.content_image_proj_model(clip_image_embeds)
|
851 |
+
# uncond_clip_image_embeds = self.image_encoder(
|
852 |
+
# torch.zeros_like(clip_image), output_hidden_states=True
|
853 |
+
# ).hidden_states[-2]
|
854 |
+
# uncond_image_prompt_embeds = self.content_image_proj_model(uncond_clip_image_embeds)
|
855 |
+
# return image_prompt_embeds, uncond_image_prompt_embeds
|
856 |
+
|
857 |
+
|
858 |
+
class IPAdapterXL(IPAdapter):
|
859 |
+
"""SDXL"""
|
860 |
+
|
861 |
+
def generate(
|
862 |
+
self,
|
863 |
+
pil_image,
|
864 |
+
prompt=None,
|
865 |
+
negative_prompt=None,
|
866 |
+
scale=1.0,
|
867 |
+
num_samples=4,
|
868 |
+
seed=None,
|
869 |
+
num_inference_steps=30,
|
870 |
+
neg_content_emb=None,
|
871 |
+
neg_content_prompt=None,
|
872 |
+
neg_content_scale=1.0,
|
873 |
+
**kwargs,
|
874 |
+
):
|
875 |
+
self.set_scale(scale)
|
876 |
+
|
877 |
+
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
878 |
+
|
879 |
+
if prompt is None:
|
880 |
+
prompt = "best quality, high quality"
|
881 |
+
if negative_prompt is None:
|
882 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
883 |
+
|
884 |
+
if not isinstance(prompt, List):
|
885 |
+
prompt = [prompt] * num_prompts
|
886 |
+
if not isinstance(negative_prompt, List):
|
887 |
+
negative_prompt = [negative_prompt] * num_prompts
|
888 |
+
|
889 |
+
if neg_content_emb is None:
|
890 |
+
if neg_content_prompt is not None:
|
891 |
+
with torch.inference_mode():
|
892 |
+
(
|
893 |
+
prompt_embeds_, # torch.Size([1, 77, 2048])
|
894 |
+
negative_prompt_embeds_,
|
895 |
+
pooled_prompt_embeds_, # torch.Size([1, 1280])
|
896 |
+
negative_pooled_prompt_embeds_,
|
897 |
+
) = self.pipe.encode_prompt(
|
898 |
+
neg_content_prompt,
|
899 |
+
num_images_per_prompt=num_samples,
|
900 |
+
do_classifier_free_guidance=True,
|
901 |
+
negative_prompt=negative_prompt,
|
902 |
+
)
|
903 |
+
pooled_prompt_embeds_ *= neg_content_scale
|
904 |
+
else:
|
905 |
+
pooled_prompt_embeds_ = neg_content_emb
|
906 |
+
else:
|
907 |
+
pooled_prompt_embeds_ = None
|
908 |
+
|
909 |
+
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image,
|
910 |
+
content_prompt_embeds=pooled_prompt_embeds_)
|
911 |
+
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
912 |
+
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
913 |
+
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
914 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
915 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
916 |
+
|
917 |
+
with torch.inference_mode():
|
918 |
+
(
|
919 |
+
prompt_embeds,
|
920 |
+
negative_prompt_embeds,
|
921 |
+
pooled_prompt_embeds,
|
922 |
+
negative_pooled_prompt_embeds,
|
923 |
+
) = self.pipe.encode_prompt(
|
924 |
+
prompt,
|
925 |
+
num_images_per_prompt=num_samples,
|
926 |
+
do_classifier_free_guidance=True,
|
927 |
+
negative_prompt=negative_prompt,
|
928 |
+
)
|
929 |
+
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
930 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
931 |
+
|
932 |
+
self.generator = get_generator(seed, self.device)
|
933 |
+
|
934 |
+
images = self.pipe(
|
935 |
+
prompt_embeds=prompt_embeds,
|
936 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
937 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
938 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
939 |
+
num_inference_steps=num_inference_steps,
|
940 |
+
generator=self.generator,
|
941 |
+
**kwargs,
|
942 |
+
).images
|
943 |
+
|
944 |
+
return images
|
945 |
+
|
946 |
+
|
947 |
+
class IPAdapterPlus(IPAdapter):
|
948 |
+
"""IP-Adapter with fine-grained features"""
|
949 |
+
|
950 |
+
def init_proj(self):
|
951 |
+
image_proj_model = Resampler(
|
952 |
+
dim=self.pipe.unet.config.cross_attention_dim,
|
953 |
+
depth=4,
|
954 |
+
dim_head=64,
|
955 |
+
heads=12,
|
956 |
+
num_queries=self.num_tokens,
|
957 |
+
embedding_dim=self.image_encoder.config.hidden_size,
|
958 |
+
output_dim=self.pipe.unet.config.cross_attention_dim,
|
959 |
+
ff_mult=4,
|
960 |
+
).to(self.device, dtype=torch.float16)
|
961 |
+
return image_proj_model
|
962 |
+
|
963 |
+
@torch.inference_mode()
|
964 |
+
def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
|
965 |
+
if isinstance(pil_image, Image.Image):
|
966 |
+
pil_image = [pil_image]
|
967 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
968 |
+
clip_image = clip_image.to(self.device, dtype=torch.float16)
|
969 |
+
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
970 |
+
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
971 |
+
uncond_clip_image_embeds = self.image_encoder(
|
972 |
+
torch.zeros_like(clip_image), output_hidden_states=True
|
973 |
+
).hidden_states[-2]
|
974 |
+
uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
|
975 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
976 |
+
|
977 |
+
|
978 |
+
class IPAdapterFull(IPAdapterPlus):
|
979 |
+
"""IP-Adapter with full features"""
|
980 |
+
|
981 |
+
def init_proj(self):
|
982 |
+
image_proj_model = MLPProjModel(
|
983 |
+
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
984 |
+
clip_embeddings_dim=self.image_encoder.config.hidden_size,
|
985 |
+
).to(self.device, dtype=torch.float16)
|
986 |
+
return image_proj_model
|
987 |
+
|
988 |
+
|
989 |
+
class IPAdapterPlusXL(IPAdapter):
|
990 |
+
"""SDXL"""
|
991 |
+
|
992 |
+
def init_proj(self):
|
993 |
+
image_proj_model = Resampler(
|
994 |
+
dim=1280,
|
995 |
+
depth=4,
|
996 |
+
dim_head=64,
|
997 |
+
heads=20,
|
998 |
+
num_queries=self.num_tokens,
|
999 |
+
embedding_dim=self.image_encoder.config.hidden_size,
|
1000 |
+
output_dim=self.pipe.unet.config.cross_attention_dim,
|
1001 |
+
ff_mult=4,
|
1002 |
+
).to(self.device, dtype=torch.float16)
|
1003 |
+
return image_proj_model
|
1004 |
+
|
1005 |
+
@torch.inference_mode()
|
1006 |
+
def get_image_embeds(self, pil_image):
|
1007 |
+
if isinstance(pil_image, Image.Image):
|
1008 |
+
pil_image = [pil_image]
|
1009 |
+
clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
|
1010 |
+
clip_image = clip_image.to(self.device, dtype=torch.float16)
|
1011 |
+
clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
|
1012 |
+
image_prompt_embeds = self.image_proj_model(clip_image_embeds)
|
1013 |
+
uncond_clip_image_embeds = self.image_encoder(
|
1014 |
+
torch.zeros_like(clip_image), output_hidden_states=True
|
1015 |
+
).hidden_states[-2]
|
1016 |
+
uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
|
1017 |
+
return image_prompt_embeds, uncond_image_prompt_embeds
|
1018 |
+
|
1019 |
+
def generate(
|
1020 |
+
self,
|
1021 |
+
pil_image,
|
1022 |
+
prompt=None,
|
1023 |
+
negative_prompt=None,
|
1024 |
+
scale=1.0,
|
1025 |
+
num_samples=4,
|
1026 |
+
seed=None,
|
1027 |
+
num_inference_steps=30,
|
1028 |
+
**kwargs,
|
1029 |
+
):
|
1030 |
+
self.set_scale(scale)
|
1031 |
+
|
1032 |
+
num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
1033 |
+
|
1034 |
+
if prompt is None:
|
1035 |
+
prompt = "best quality, high quality"
|
1036 |
+
if negative_prompt is None:
|
1037 |
+
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
1038 |
+
|
1039 |
+
if not isinstance(prompt, List):
|
1040 |
+
prompt = [prompt] * num_prompts
|
1041 |
+
if not isinstance(negative_prompt, List):
|
1042 |
+
negative_prompt = [negative_prompt] * num_prompts
|
1043 |
+
|
1044 |
+
image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
|
1045 |
+
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
1046 |
+
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
1047 |
+
image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
1048 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
|
1049 |
+
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
|
1050 |
+
|
1051 |
+
with torch.inference_mode():
|
1052 |
+
(
|
1053 |
+
prompt_embeds,
|
1054 |
+
negative_prompt_embeds,
|
1055 |
+
pooled_prompt_embeds,
|
1056 |
+
negative_pooled_prompt_embeds,
|
1057 |
+
) = self.pipe.encode_prompt(
|
1058 |
+
prompt,
|
1059 |
+
num_images_per_prompt=num_samples,
|
1060 |
+
do_classifier_free_guidance=True,
|
1061 |
+
negative_prompt=negative_prompt,
|
1062 |
+
)
|
1063 |
+
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
1064 |
+
negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
|
1065 |
+
|
1066 |
+
generator = get_generator(seed, self.device)
|
1067 |
+
|
1068 |
+
images = self.pipe(
|
1069 |
+
prompt_embeds=prompt_embeds,
|
1070 |
+
negative_prompt_embeds=negative_prompt_embeds,
|
1071 |
+
pooled_prompt_embeds=pooled_prompt_embeds,
|
1072 |
+
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
1073 |
+
num_inference_steps=num_inference_steps,
|
1074 |
+
generator=generator,
|
1075 |
+
**kwargs,
|
1076 |
+
).images
|
1077 |
+
|
1078 |
+
return images
|
ip_adapter/resampler.py
ADDED
@@ -0,0 +1,158 @@
|
|
|
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|
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|
|
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|
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
|
2 |
+
# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
|
3 |
+
|
4 |
+
import math
|
5 |
+
|
6 |
+
import torch
|
7 |
+
import torch.nn as nn
|
8 |
+
from einops import rearrange
|
9 |
+
from einops.layers.torch import Rearrange
|
10 |
+
|
11 |
+
|
12 |
+
# FFN
|
13 |
+
def FeedForward(dim, mult=4):
|
14 |
+
inner_dim = int(dim * mult)
|
15 |
+
return nn.Sequential(
|
16 |
+
nn.LayerNorm(dim),
|
17 |
+
nn.Linear(dim, inner_dim, bias=False),
|
18 |
+
nn.GELU(),
|
19 |
+
nn.Linear(inner_dim, dim, bias=False),
|
20 |
+
)
|
21 |
+
|
22 |
+
|
23 |
+
def reshape_tensor(x, heads):
|
24 |
+
bs, length, width = x.shape
|
25 |
+
# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
|
26 |
+
x = x.view(bs, length, heads, -1)
|
27 |
+
# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
|
28 |
+
x = x.transpose(1, 2)
|
29 |
+
# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
|
30 |
+
x = x.reshape(bs, heads, length, -1)
|
31 |
+
return x
|
32 |
+
|
33 |
+
|
34 |
+
class PerceiverAttention(nn.Module):
|
35 |
+
def __init__(self, *, dim, dim_head=64, heads=8):
|
36 |
+
super().__init__()
|
37 |
+
self.scale = dim_head**-0.5
|
38 |
+
self.dim_head = dim_head
|
39 |
+
self.heads = heads
|
40 |
+
inner_dim = dim_head * heads
|
41 |
+
|
42 |
+
self.norm1 = nn.LayerNorm(dim)
|
43 |
+
self.norm2 = nn.LayerNorm(dim)
|
44 |
+
|
45 |
+
self.to_q = nn.Linear(dim, inner_dim, bias=False)
|
46 |
+
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
|
47 |
+
self.to_out = nn.Linear(inner_dim, dim, bias=False)
|
48 |
+
|
49 |
+
def forward(self, x, latents):
|
50 |
+
"""
|
51 |
+
Args:
|
52 |
+
x (torch.Tensor): image features
|
53 |
+
shape (b, n1, D)
|
54 |
+
latent (torch.Tensor): latent features
|
55 |
+
shape (b, n2, D)
|
56 |
+
"""
|
57 |
+
x = self.norm1(x)
|
58 |
+
latents = self.norm2(latents)
|
59 |
+
|
60 |
+
b, l, _ = latents.shape
|
61 |
+
|
62 |
+
q = self.to_q(latents)
|
63 |
+
kv_input = torch.cat((x, latents), dim=-2)
|
64 |
+
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
65 |
+
|
66 |
+
q = reshape_tensor(q, self.heads)
|
67 |
+
k = reshape_tensor(k, self.heads)
|
68 |
+
v = reshape_tensor(v, self.heads)
|
69 |
+
|
70 |
+
# attention
|
71 |
+
scale = 1 / math.sqrt(math.sqrt(self.dim_head))
|
72 |
+
weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards
|
73 |
+
weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
|
74 |
+
out = weight @ v
|
75 |
+
|
76 |
+
out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
|
77 |
+
|
78 |
+
return self.to_out(out)
|
79 |
+
|
80 |
+
|
81 |
+
class Resampler(nn.Module):
|
82 |
+
def __init__(
|
83 |
+
self,
|
84 |
+
dim=1024,
|
85 |
+
depth=8,
|
86 |
+
dim_head=64,
|
87 |
+
heads=16,
|
88 |
+
num_queries=8,
|
89 |
+
embedding_dim=768,
|
90 |
+
output_dim=1024,
|
91 |
+
ff_mult=4,
|
92 |
+
max_seq_len: int = 257, # CLIP tokens + CLS token
|
93 |
+
apply_pos_emb: bool = False,
|
94 |
+
num_latents_mean_pooled: int = 0, # number of latents derived from mean pooled representation of the sequence
|
95 |
+
):
|
96 |
+
super().__init__()
|
97 |
+
self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
|
98 |
+
|
99 |
+
self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
|
100 |
+
|
101 |
+
self.proj_in = nn.Linear(embedding_dim, dim)
|
102 |
+
|
103 |
+
self.proj_out = nn.Linear(dim, output_dim)
|
104 |
+
self.norm_out = nn.LayerNorm(output_dim)
|
105 |
+
|
106 |
+
self.to_latents_from_mean_pooled_seq = (
|
107 |
+
nn.Sequential(
|
108 |
+
nn.LayerNorm(dim),
|
109 |
+
nn.Linear(dim, dim * num_latents_mean_pooled),
|
110 |
+
Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
|
111 |
+
)
|
112 |
+
if num_latents_mean_pooled > 0
|
113 |
+
else None
|
114 |
+
)
|
115 |
+
|
116 |
+
self.layers = nn.ModuleList([])
|
117 |
+
for _ in range(depth):
|
118 |
+
self.layers.append(
|
119 |
+
nn.ModuleList(
|
120 |
+
[
|
121 |
+
PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
|
122 |
+
FeedForward(dim=dim, mult=ff_mult),
|
123 |
+
]
|
124 |
+
)
|
125 |
+
)
|
126 |
+
|
127 |
+
def forward(self, x):
|
128 |
+
if self.pos_emb is not None:
|
129 |
+
n, device = x.shape[1], x.device
|
130 |
+
pos_emb = self.pos_emb(torch.arange(n, device=device))
|
131 |
+
x = x + pos_emb
|
132 |
+
|
133 |
+
latents = self.latents.repeat(x.size(0), 1, 1)
|
134 |
+
|
135 |
+
x = self.proj_in(x)
|
136 |
+
|
137 |
+
if self.to_latents_from_mean_pooled_seq:
|
138 |
+
meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
|
139 |
+
meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
|
140 |
+
latents = torch.cat((meanpooled_latents, latents), dim=-2)
|
141 |
+
|
142 |
+
for attn, ff in self.layers:
|
143 |
+
latents = attn(x, latents) + latents
|
144 |
+
latents = ff(latents) + latents
|
145 |
+
|
146 |
+
latents = self.proj_out(latents)
|
147 |
+
return self.norm_out(latents)
|
148 |
+
|
149 |
+
|
150 |
+
def masked_mean(t, *, dim, mask=None):
|
151 |
+
if mask is None:
|
152 |
+
return t.mean(dim=dim)
|
153 |
+
|
154 |
+
denom = mask.sum(dim=dim, keepdim=True)
|
155 |
+
mask = rearrange(mask, "b n -> b n 1")
|
156 |
+
masked_t = t.masked_fill(~mask, 0.0)
|
157 |
+
|
158 |
+
return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)
|
ip_adapter/utils.py
ADDED
@@ -0,0 +1,142 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn.functional as F
|
3 |
+
import numpy as np
|
4 |
+
from PIL import Image
|
5 |
+
|
6 |
+
BLOCKS = {
|
7 |
+
'content': ['down_blocks'],
|
8 |
+
'style': ["up_blocks"],
|
9 |
+
|
10 |
+
}
|
11 |
+
|
12 |
+
controlnet_BLOCKS = {
|
13 |
+
'content': [],
|
14 |
+
'style': ["down_blocks"],
|
15 |
+
}
|
16 |
+
|
17 |
+
|
18 |
+
def resize_width_height(width, height, min_short_side=512, max_long_side=1024):
|
19 |
+
|
20 |
+
if width < height:
|
21 |
+
|
22 |
+
if width < min_short_side:
|
23 |
+
scale_factor = min_short_side / width
|
24 |
+
new_width = min_short_side
|
25 |
+
new_height = int(height * scale_factor)
|
26 |
+
else:
|
27 |
+
new_width, new_height = width, height
|
28 |
+
else:
|
29 |
+
|
30 |
+
if height < min_short_side:
|
31 |
+
scale_factor = min_short_side / height
|
32 |
+
new_width = int(width * scale_factor)
|
33 |
+
new_height = min_short_side
|
34 |
+
else:
|
35 |
+
new_width, new_height = width, height
|
36 |
+
|
37 |
+
if max(new_width, new_height) > max_long_side:
|
38 |
+
scale_factor = max_long_side / max(new_width, new_height)
|
39 |
+
new_width = int(new_width * scale_factor)
|
40 |
+
new_height = int(new_height * scale_factor)
|
41 |
+
return new_width, new_height
|
42 |
+
|
43 |
+
def resize_content(content_image):
|
44 |
+
max_long_side = 1024
|
45 |
+
min_short_side = 1024
|
46 |
+
|
47 |
+
new_width, new_height = resize_width_height(content_image.size[0], content_image.size[1],
|
48 |
+
min_short_side=min_short_side, max_long_side=max_long_side)
|
49 |
+
height = new_height // 16 * 16
|
50 |
+
width = new_width // 16 * 16
|
51 |
+
content_image = content_image.resize((width, height))
|
52 |
+
|
53 |
+
return width,height,content_image
|
54 |
+
|
55 |
+
attn_maps = {}
|
56 |
+
def hook_fn(name):
|
57 |
+
def forward_hook(module, input, output):
|
58 |
+
if hasattr(module.processor, "attn_map"):
|
59 |
+
attn_maps[name] = module.processor.attn_map
|
60 |
+
del module.processor.attn_map
|
61 |
+
|
62 |
+
return forward_hook
|
63 |
+
|
64 |
+
def register_cross_attention_hook(unet):
|
65 |
+
for name, module in unet.named_modules():
|
66 |
+
if name.split('.')[-1].startswith('attn2'):
|
67 |
+
module.register_forward_hook(hook_fn(name))
|
68 |
+
|
69 |
+
return unet
|
70 |
+
|
71 |
+
def upscale(attn_map, target_size):
|
72 |
+
attn_map = torch.mean(attn_map, dim=0)
|
73 |
+
attn_map = attn_map.permute(1,0)
|
74 |
+
temp_size = None
|
75 |
+
|
76 |
+
for i in range(0,5):
|
77 |
+
scale = 2 ** i
|
78 |
+
if ( target_size[0] // scale ) * ( target_size[1] // scale) == attn_map.shape[1]*64:
|
79 |
+
temp_size = (target_size[0]//(scale*8), target_size[1]//(scale*8))
|
80 |
+
break
|
81 |
+
|
82 |
+
assert temp_size is not None, "temp_size cannot is None"
|
83 |
+
|
84 |
+
attn_map = attn_map.view(attn_map.shape[0], *temp_size)
|
85 |
+
|
86 |
+
attn_map = F.interpolate(
|
87 |
+
attn_map.unsqueeze(0).to(dtype=torch.float32),
|
88 |
+
size=target_size,
|
89 |
+
mode='bilinear',
|
90 |
+
align_corners=False
|
91 |
+
)[0]
|
92 |
+
|
93 |
+
attn_map = torch.softmax(attn_map, dim=0)
|
94 |
+
return attn_map
|
95 |
+
def get_net_attn_map(image_size, batch_size=2, instance_or_negative=False, detach=True):
|
96 |
+
|
97 |
+
idx = 0 if instance_or_negative else 1
|
98 |
+
net_attn_maps = []
|
99 |
+
|
100 |
+
for name, attn_map in attn_maps.items():
|
101 |
+
attn_map = attn_map.cpu() if detach else attn_map
|
102 |
+
attn_map = torch.chunk(attn_map, batch_size)[idx].squeeze()
|
103 |
+
attn_map = upscale(attn_map, image_size)
|
104 |
+
net_attn_maps.append(attn_map)
|
105 |
+
|
106 |
+
net_attn_maps = torch.mean(torch.stack(net_attn_maps,dim=0),dim=0)
|
107 |
+
|
108 |
+
return net_attn_maps
|
109 |
+
|
110 |
+
def attnmaps2images(net_attn_maps):
|
111 |
+
|
112 |
+
#total_attn_scores = 0
|
113 |
+
images = []
|
114 |
+
|
115 |
+
for attn_map in net_attn_maps:
|
116 |
+
attn_map = attn_map.cpu().numpy()
|
117 |
+
#total_attn_scores += attn_map.mean().item()
|
118 |
+
|
119 |
+
normalized_attn_map = (attn_map - np.min(attn_map)) / (np.max(attn_map) - np.min(attn_map)) * 255
|
120 |
+
normalized_attn_map = normalized_attn_map.astype(np.uint8)
|
121 |
+
#print("norm: ", normalized_attn_map.shape)
|
122 |
+
image = Image.fromarray(normalized_attn_map)
|
123 |
+
|
124 |
+
#image = fix_save_attn_map(attn_map)
|
125 |
+
images.append(image)
|
126 |
+
|
127 |
+
#print(total_attn_scores)
|
128 |
+
return images
|
129 |
+
def is_torch2_available():
|
130 |
+
return hasattr(F, "scaled_dot_product_attention")
|
131 |
+
|
132 |
+
def get_generator(seed, device):
|
133 |
+
|
134 |
+
if seed is not None:
|
135 |
+
if isinstance(seed, list):
|
136 |
+
generator = [torch.Generator(device).manual_seed(seed_item) for seed_item in seed]
|
137 |
+
else:
|
138 |
+
generator = torch.Generator(device).manual_seed(seed)
|
139 |
+
else:
|
140 |
+
generator = None
|
141 |
+
|
142 |
+
return generator
|
requirements.txt
ADDED
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
diffusers==0.25.1
|
2 |
+
torch==2.0.1
|
3 |
+
torchaudio==2.0.2
|
4 |
+
torchvision==0.15.2
|
5 |
+
transformers==4.40.2
|
6 |
+
accelerate
|
7 |
+
safetensors
|
8 |
+
einops
|
9 |
+
spaces==0.19.4
|
10 |
+
omegaconf
|
11 |
+
peft
|
12 |
+
huggingface-hub==0.24.5
|
13 |
+
opencv-python
|
14 |
+
insightface
|
15 |
+
gradio
|
16 |
+
controlnet_aux
|
17 |
+
gdown
|
18 |
+
peft
|