Spaces:
Running
on
Zero
Running
on
Zero
Commit
·
154c805
1
Parent(s):
86c729f
modify app.py
Browse files- ace_inference.py +356 -0
- example.py +370 -0
- model/__init__.py +1 -0
- model/flux.py +1064 -0
- model/layers.py +356 -0
- utils.py +95 -0
ace_inference.py
ADDED
@@ -0,0 +1,356 @@
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1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
# Copyright (c) Alibaba, Inc. and its affiliates.
|
3 |
+
import copy
|
4 |
+
import math
|
5 |
+
import random
|
6 |
+
|
7 |
+
import numpy as np
|
8 |
+
import torch
|
9 |
+
import torch.nn as nn
|
10 |
+
import torch.nn.functional as F
|
11 |
+
import torchvision.transforms.functional as TF
|
12 |
+
from PIL import Image
|
13 |
+
import torchvision.transforms as T
|
14 |
+
from scepter.modules.model.registry import DIFFUSIONS
|
15 |
+
from scepter.modules.model.utils.basic_utils import check_list_of_list
|
16 |
+
from scepter.modules.model.utils.basic_utils import \
|
17 |
+
pack_imagelist_into_tensor_v2 as pack_imagelist_into_tensor
|
18 |
+
from scepter.modules.model.utils.basic_utils import (
|
19 |
+
to_device, unpack_tensor_into_imagelist)
|
20 |
+
from scepter.modules.utils.distribute import we
|
21 |
+
from scepter.modules.utils.logger import get_logger
|
22 |
+
|
23 |
+
from scepter.modules.inference.diffusion_inference import DiffusionInference, get_model
|
24 |
+
|
25 |
+
|
26 |
+
def process_edit_image(images,
|
27 |
+
masks,
|
28 |
+
tasks,
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29 |
+
max_seq_len=1024,
|
30 |
+
max_aspect_ratio=4,
|
31 |
+
d=16,
|
32 |
+
**kwargs):
|
33 |
+
|
34 |
+
if not isinstance(images, list):
|
35 |
+
images = [images]
|
36 |
+
if not isinstance(masks, list):
|
37 |
+
masks = [masks]
|
38 |
+
if not isinstance(tasks, list):
|
39 |
+
tasks = [tasks]
|
40 |
+
|
41 |
+
img_tensors = []
|
42 |
+
mask_tensors = []
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43 |
+
for img, mask, task in zip(images, masks, tasks):
|
44 |
+
if mask is None or mask == '':
|
45 |
+
mask = Image.new('L', img.size, 0)
|
46 |
+
W, H = img.size
|
47 |
+
if H / W > max_aspect_ratio:
|
48 |
+
img = TF.center_crop(img, [int(max_aspect_ratio * W), W])
|
49 |
+
mask = TF.center_crop(mask, [int(max_aspect_ratio * W), W])
|
50 |
+
elif W / H > max_aspect_ratio:
|
51 |
+
img = TF.center_crop(img, [H, int(max_aspect_ratio * H)])
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52 |
+
mask = TF.center_crop(mask, [H, int(max_aspect_ratio * H)])
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53 |
+
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54 |
+
H, W = img.height, img.width
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55 |
+
scale = min(1.0, math.sqrt(max_seq_len / ((H / d) * (W / d))))
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56 |
+
rH = int(H * scale) // d * d # ensure divisible by self.d
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57 |
+
rW = int(W * scale) // d * d
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58 |
+
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59 |
+
img = TF.resize(img, (rH, rW),
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60 |
+
interpolation=TF.InterpolationMode.BICUBIC)
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61 |
+
mask = TF.resize(mask, (rH, rW),
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62 |
+
interpolation=TF.InterpolationMode.NEAREST_EXACT)
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63 |
+
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64 |
+
mask = np.asarray(mask)
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65 |
+
mask = np.where(mask > 128, 1, 0)
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66 |
+
mask = mask.astype(
|
67 |
+
np.float32) if np.any(mask) else np.ones_like(mask).astype(
|
68 |
+
np.float32)
|
69 |
+
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70 |
+
img_tensor = TF.to_tensor(img).to(we.device_id)
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71 |
+
img_tensor = TF.normalize(img_tensor,
|
72 |
+
mean=[0.5, 0.5, 0.5],
|
73 |
+
std=[0.5, 0.5, 0.5])
|
74 |
+
mask_tensor = TF.to_tensor(mask).to(we.device_id)
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75 |
+
if task in ['inpainting', 'Try On', 'Inpainting']:
|
76 |
+
mask_indicator = mask_tensor.repeat(3, 1, 1)
|
77 |
+
img_tensor[mask_indicator == 1] = -1.0
|
78 |
+
img_tensors.append(img_tensor)
|
79 |
+
mask_tensors.append(mask_tensor)
|
80 |
+
return img_tensors, mask_tensors
|
81 |
+
|
82 |
+
class TextEmbedding(nn.Module):
|
83 |
+
def __init__(self, embedding_shape):
|
84 |
+
super().__init__()
|
85 |
+
self.pos = nn.Parameter(data=torch.zeros(embedding_shape))
|
86 |
+
|
87 |
+
class ACEFluxLCInference(DiffusionInference):
|
88 |
+
def __init__(self, logger=None):
|
89 |
+
if logger is None:
|
90 |
+
logger = get_logger(name='scepter')
|
91 |
+
self.logger = logger
|
92 |
+
self.loaded_model = {}
|
93 |
+
self.loaded_model_name = [
|
94 |
+
'diffusion_model', 'first_stage_model', 'cond_stage_model', 'ref_cond_stage_model'
|
95 |
+
]
|
96 |
+
|
97 |
+
def init_from_cfg(self, cfg):
|
98 |
+
self.name = cfg.NAME
|
99 |
+
self.is_default = cfg.get('IS_DEFAULT', False)
|
100 |
+
self.use_dynamic_model = cfg.get('USE_DYNAMIC_MODEL', True)
|
101 |
+
module_paras = self.load_default(cfg.get('DEFAULT_PARAS', None))
|
102 |
+
assert cfg.have('MODEL')
|
103 |
+
self.size_factor = cfg.get('SIZE_FACTOR', 8)
|
104 |
+
self.diffusion_model = self.infer_model(
|
105 |
+
cfg.MODEL.DIFFUSION_MODEL, module_paras.get(
|
106 |
+
'DIFFUSION_MODEL',
|
107 |
+
None)) if cfg.MODEL.have('DIFFUSION_MODEL') else None
|
108 |
+
self.first_stage_model = self.infer_model(
|
109 |
+
cfg.MODEL.FIRST_STAGE_MODEL,
|
110 |
+
module_paras.get(
|
111 |
+
'FIRST_STAGE_MODEL',
|
112 |
+
None)) if cfg.MODEL.have('FIRST_STAGE_MODEL') else None
|
113 |
+
self.cond_stage_model = self.infer_model(
|
114 |
+
cfg.MODEL.COND_STAGE_MODEL,
|
115 |
+
module_paras.get(
|
116 |
+
'COND_STAGE_MODEL',
|
117 |
+
None)) if cfg.MODEL.have('COND_STAGE_MODEL') else None
|
118 |
+
|
119 |
+
self.ref_cond_stage_model = self.infer_model(
|
120 |
+
cfg.MODEL.REF_COND_STAGE_MODEL,
|
121 |
+
module_paras.get(
|
122 |
+
'REF_COND_STAGE_MODEL',
|
123 |
+
None)) if cfg.MODEL.have('REF_COND_STAGE_MODEL') else None
|
124 |
+
|
125 |
+
self.diffusion = DIFFUSIONS.build(cfg.MODEL.DIFFUSION,
|
126 |
+
logger=self.logger)
|
127 |
+
self.interpolate_func = lambda x: (F.interpolate(
|
128 |
+
x.unsqueeze(0),
|
129 |
+
scale_factor=1 / self.size_factor,
|
130 |
+
mode='nearest-exact') if x is not None else None)
|
131 |
+
|
132 |
+
self.max_seq_length = cfg.get("MAX_SEQ_LENGTH", 4096)
|
133 |
+
self.src_max_seq_length = cfg.get("SRC_MAX_SEQ_LENGTH", 1024)
|
134 |
+
self.image_token = cfg.MODEL.get("IMAGE_TOKEN", "<img>")
|
135 |
+
|
136 |
+
self.text_indentifers = cfg.MODEL.get('TEXT_IDENTIFIER', [])
|
137 |
+
self.use_text_pos_embeddings = cfg.MODEL.get('USE_TEXT_POS_EMBEDDINGS',
|
138 |
+
False)
|
139 |
+
if self.use_text_pos_embeddings:
|
140 |
+
self.text_position_embeddings = TextEmbedding(
|
141 |
+
(10, 4096)).eval().requires_grad_(False).to(we.device_id)
|
142 |
+
else:
|
143 |
+
self.text_position_embeddings = None
|
144 |
+
|
145 |
+
if not self.use_dynamic_model:
|
146 |
+
self.dynamic_load(self.first_stage_model, 'first_stage_model')
|
147 |
+
self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
|
148 |
+
if self.ref_cond_stage_model is not None: self.dynamic_load(self.ref_cond_stage_model, 'ref_cond_stage_model')
|
149 |
+
self.dynamic_load(self.diffusion_model, 'diffusion_model')
|
150 |
+
|
151 |
+
def upscale_resize(self, image, interpolation=T.InterpolationMode.BILINEAR):
|
152 |
+
c, H, W = image.shape
|
153 |
+
scale = max(1.0, math.sqrt(self.max_seq_length / ((H / 16) * (W / 16))))
|
154 |
+
rH = int(H * scale) // 16 * 16 # ensure divisible by self.d
|
155 |
+
rW = int(W * scale) // 16 * 16
|
156 |
+
image = T.Resize((rH, rW), interpolation=interpolation, antialias=True)(image)
|
157 |
+
return image
|
158 |
+
|
159 |
+
|
160 |
+
@torch.no_grad()
|
161 |
+
def encode_first_stage(self, x, **kwargs):
|
162 |
+
_, dtype = self.get_function_info(self.first_stage_model, 'encode')
|
163 |
+
with torch.autocast('cuda',
|
164 |
+
enabled=dtype in ('float16', 'bfloat16'),
|
165 |
+
dtype=getattr(torch, dtype)):
|
166 |
+
def run_one_image(u):
|
167 |
+
zu = get_model(self.first_stage_model).encode(u)
|
168 |
+
if isinstance(zu, (tuple, list)):
|
169 |
+
zu = zu[0]
|
170 |
+
return zu
|
171 |
+
|
172 |
+
z = [run_one_image(u.unsqueeze(0) if u.dim() == 3 else u) for u in x]
|
173 |
+
return z
|
174 |
+
|
175 |
+
|
176 |
+
@torch.no_grad()
|
177 |
+
def decode_first_stage(self, z):
|
178 |
+
_, dtype = self.get_function_info(self.first_stage_model, 'decode')
|
179 |
+
with torch.autocast('cuda',
|
180 |
+
enabled=dtype in ('float16', 'bfloat16'),
|
181 |
+
dtype=getattr(torch, dtype)):
|
182 |
+
return [get_model(self.first_stage_model).decode(zu) for zu in z]
|
183 |
+
|
184 |
+
def noise_sample(self, num_samples, h, w, seed, device = None, dtype = torch.bfloat16):
|
185 |
+
noise = torch.randn(
|
186 |
+
num_samples,
|
187 |
+
16,
|
188 |
+
# allow for packing
|
189 |
+
2 * math.ceil(h / 16),
|
190 |
+
2 * math.ceil(w / 16),
|
191 |
+
device=device,
|
192 |
+
dtype=dtype,
|
193 |
+
generator=torch.Generator(device=device).manual_seed(seed),
|
194 |
+
)
|
195 |
+
return noise
|
196 |
+
|
197 |
+
# def preprocess_prompt(self, prompt):
|
198 |
+
# prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt]
|
199 |
+
# for pp_id, pp in enumerate(prompt_):
|
200 |
+
# prompt_[pp_id] = [""] + pp
|
201 |
+
# for p_id, p in enumerate(prompt_[pp_id]):
|
202 |
+
# prompt_[pp_id][p_id] = self.image_token + self.text_indentifers[p_id] + " " + p
|
203 |
+
# prompt_[pp_id] = [f";".join(prompt_[pp_id])]
|
204 |
+
# return prompt_
|
205 |
+
|
206 |
+
@torch.no_grad()
|
207 |
+
def __call__(self,
|
208 |
+
image=None,
|
209 |
+
mask=None,
|
210 |
+
prompt='',
|
211 |
+
task=None,
|
212 |
+
negative_prompt='',
|
213 |
+
output_height=1024,
|
214 |
+
output_width=1024,
|
215 |
+
sampler='flow_euler',
|
216 |
+
sample_steps=20,
|
217 |
+
guide_scale=3.5,
|
218 |
+
seed=-1,
|
219 |
+
history_io=None,
|
220 |
+
tar_index=0,
|
221 |
+
align=0,
|
222 |
+
**kwargs):
|
223 |
+
input_image, input_mask = image, mask
|
224 |
+
seed = seed if seed >= 0 else random.randint(0, 2**32 - 1)
|
225 |
+
if input_image is not None:
|
226 |
+
# assert isinstance(input_image, list) and isinstance(input_mask, list)
|
227 |
+
if task is None:
|
228 |
+
task = [''] * len(input_image)
|
229 |
+
if not isinstance(prompt, list):
|
230 |
+
prompt = [prompt] * len(input_image)
|
231 |
+
prompt = [
|
232 |
+
pp.replace('{image}', f'{{image{i}}}') if i > 0 else pp
|
233 |
+
for i, pp in enumerate(prompt)
|
234 |
+
]
|
235 |
+
edit_image, edit_image_mask = process_edit_image(
|
236 |
+
input_image, input_mask, task, max_seq_len=self.src_max_seq_length)
|
237 |
+
image, image_mask = self.upscale_resize(edit_image[tar_index]), self.upscale_resize(edit_image_mask[
|
238 |
+
tar_index])
|
239 |
+
# edit_image, edit_image_mask = [[self.upscale_resize(i) for i in edit_image]], [[self.upscale_resize(i) for i in edit_image_mask]]
|
240 |
+
# image, image_mask = edit_image[tar_index], edit_image_mask[tar_index]
|
241 |
+
edit_image, edit_image_mask = [edit_image], [edit_image_mask]
|
242 |
+
else:
|
243 |
+
edit_image = edit_image_mask = [[]]
|
244 |
+
image = torch.zeros(
|
245 |
+
size=[3, int(output_height),
|
246 |
+
int(output_width)])
|
247 |
+
image_mask = torch.ones(
|
248 |
+
size=[1, int(output_height),
|
249 |
+
int(output_width)])
|
250 |
+
if not isinstance(prompt, list):
|
251 |
+
prompt = [prompt]
|
252 |
+
|
253 |
+
image, image_mask, prompt = [image], [image_mask], [prompt],
|
254 |
+
align = [align for p in prompt] if isinstance(align, int) else align
|
255 |
+
|
256 |
+
assert check_list_of_list(prompt) and check_list_of_list(
|
257 |
+
edit_image) and check_list_of_list(edit_image_mask)
|
258 |
+
# negative prompt is not used
|
259 |
+
image = to_device(image)
|
260 |
+
ctx = {}
|
261 |
+
# Get Noise Shape
|
262 |
+
self.dynamic_load(self.first_stage_model, 'first_stage_model')
|
263 |
+
x = self.encode_first_stage(image)
|
264 |
+
self.dynamic_unload(self.first_stage_model,
|
265 |
+
'first_stage_model',
|
266 |
+
skip_loaded=not self.use_dynamic_model)
|
267 |
+
|
268 |
+
g = torch.Generator(device=we.device_id).manual_seed(seed)
|
269 |
+
|
270 |
+
noise = [
|
271 |
+
torch.randn((1, 16, i.shape[2], i.shape[3]), device=we.device_id, dtype=torch.bfloat16).normal_(generator=g)
|
272 |
+
for i in x
|
273 |
+
]
|
274 |
+
noise, x_shapes = pack_imagelist_into_tensor(noise)
|
275 |
+
ctx['x_shapes'] = x_shapes
|
276 |
+
ctx['align'] = align
|
277 |
+
|
278 |
+
image_mask = to_device(image_mask, strict=False)
|
279 |
+
cond_mask = [self.interpolate_func(i) for i in image_mask
|
280 |
+
] if image_mask is not None else [None] * len(image)
|
281 |
+
ctx['x_mask'] = cond_mask
|
282 |
+
# Encode Prompt
|
283 |
+
instruction_prompt = [[pp[-1]] if "{image}" in pp[-1] else ["{image} " + pp[-1]] for pp in prompt]
|
284 |
+
self.dynamic_load(self.cond_stage_model, 'cond_stage_model')
|
285 |
+
function_name, dtype = self.get_function_info(self.cond_stage_model)
|
286 |
+
cont = getattr(get_model(self.cond_stage_model), function_name)(instruction_prompt)
|
287 |
+
cont["context"] = [ct[-1] for ct in cont["context"]]
|
288 |
+
cont["y"] = [ct[-1] for ct in cont["y"]]
|
289 |
+
self.dynamic_unload(self.cond_stage_model,
|
290 |
+
'cond_stage_model',
|
291 |
+
skip_loaded=not self.use_dynamic_model)
|
292 |
+
ctx.update(cont)
|
293 |
+
|
294 |
+
# Encode Edit Images
|
295 |
+
self.dynamic_load(self.first_stage_model, 'first_stage_model')
|
296 |
+
edit_image = [to_device(i, strict=False) for i in edit_image]
|
297 |
+
edit_image_mask = [to_device(i, strict=False) for i in edit_image_mask]
|
298 |
+
e_img, e_mask = [], []
|
299 |
+
for u, m in zip(edit_image, edit_image_mask):
|
300 |
+
if u is None:
|
301 |
+
continue
|
302 |
+
if m is None:
|
303 |
+
m = [None] * len(u)
|
304 |
+
e_img.append(self.encode_first_stage(u, **kwargs))
|
305 |
+
e_mask.append([self.interpolate_func(i) for i in m])
|
306 |
+
self.dynamic_unload(self.first_stage_model,
|
307 |
+
'first_stage_model',
|
308 |
+
skip_loaded=not self.use_dynamic_model)
|
309 |
+
ctx['edit_x'] = e_img
|
310 |
+
ctx['edit_mask'] = e_mask
|
311 |
+
# Encode Ref Images
|
312 |
+
if guide_scale is not None:
|
313 |
+
guide_scale = torch.full((noise.shape[0],), guide_scale, device=noise.device, dtype=noise.dtype)
|
314 |
+
else:
|
315 |
+
guide_scale = None
|
316 |
+
|
317 |
+
# Diffusion Process
|
318 |
+
self.dynamic_load(self.diffusion_model, 'diffusion_model')
|
319 |
+
function_name, dtype = self.get_function_info(self.diffusion_model)
|
320 |
+
with torch.autocast('cuda',
|
321 |
+
enabled=dtype in ('float16', 'bfloat16'),
|
322 |
+
dtype=getattr(torch, dtype)):
|
323 |
+
latent = self.diffusion.sample(
|
324 |
+
noise=noise,
|
325 |
+
sampler=sampler,
|
326 |
+
model=get_model(self.diffusion_model),
|
327 |
+
model_kwargs={
|
328 |
+
"cond": ctx, "guidance": guide_scale, "gc_seg": -1
|
329 |
+
},
|
330 |
+
steps=sample_steps,
|
331 |
+
show_progress=True,
|
332 |
+
guide_scale=guide_scale,
|
333 |
+
return_intermediate=None,
|
334 |
+
reverse_scale=-1,
|
335 |
+
**kwargs).float()
|
336 |
+
if self.use_dynamic_model: self.dynamic_unload(self.diffusion_model,
|
337 |
+
'diffusion_model',
|
338 |
+
skip_loaded=not self.use_dynamic_model)
|
339 |
+
|
340 |
+
# Decode to Pixel Space
|
341 |
+
self.dynamic_load(self.first_stage_model, 'first_stage_model')
|
342 |
+
samples = unpack_tensor_into_imagelist(latent, x_shapes)
|
343 |
+
x_samples = self.decode_first_stage(samples)
|
344 |
+
self.dynamic_unload(self.first_stage_model,
|
345 |
+
'first_stage_model',
|
346 |
+
skip_loaded=not self.use_dynamic_model)
|
347 |
+
x_samples = [x.squeeze(0) for x in x_samples]
|
348 |
+
|
349 |
+
imgs = [
|
350 |
+
torch.clamp((x_i.float() + 1.0) / 2.0,
|
351 |
+
min=0.0,
|
352 |
+
max=1.0).squeeze(0).permute(1, 2, 0).cpu().numpy()
|
353 |
+
for x_i in x_samples
|
354 |
+
]
|
355 |
+
imgs = [Image.fromarray((img * 255).astype(np.uint8)) for img in imgs]
|
356 |
+
return imgs
|
example.py
ADDED
@@ -0,0 +1,370 @@
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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 |
+
# -*- coding: utf-8 -*-
|
2 |
+
# Copyright (c) Alibaba, Inc. and its affiliates.
|
3 |
+
import os
|
4 |
+
from PIL import Image
|
5 |
+
from scepter.modules.utils.file_system import FS
|
6 |
+
|
7 |
+
|
8 |
+
def download_image(image, local_path=None):
|
9 |
+
if not FS.exists(local_path):
|
10 |
+
local_path = FS.get_from(image, local_path=local_path)
|
11 |
+
if local_path.split(".")[-1] in ['jpg', 'jpeg']:
|
12 |
+
im = Image.open(local_path).convert("RGB")
|
13 |
+
im.save(local_path, format='JPEG')
|
14 |
+
return local_path
|
15 |
+
|
16 |
+
|
17 |
+
def get_examples(cache_dir):
|
18 |
+
print('Downloading Examples ...')
|
19 |
+
examples = [
|
20 |
+
[
|
21 |
+
'Facial Editing',
|
22 |
+
download_image(
|
23 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e33edc106953.png?raw=true',
|
24 |
+
os.path.join(cache_dir, 'examples/e33edc106953.jpg')), None,
|
25 |
+
None, '{image} let the man smile', 6666
|
26 |
+
],
|
27 |
+
[
|
28 |
+
'Facial Editing',
|
29 |
+
download_image(
|
30 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5d2bcc91a3e9.png?raw=true',
|
31 |
+
os.path.join(cache_dir, 'examples/5d2bcc91a3e9.jpg')), None,
|
32 |
+
None, 'let the man in {image} wear sunglasses', 9999
|
33 |
+
],
|
34 |
+
[
|
35 |
+
'Facial Editing',
|
36 |
+
download_image(
|
37 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3a52eac708bd.png?raw=true',
|
38 |
+
os.path.join(cache_dir, 'examples/3a52eac708bd.jpg')), None,
|
39 |
+
None, '{image} red hair', 9999
|
40 |
+
],
|
41 |
+
[
|
42 |
+
'Facial Editing',
|
43 |
+
download_image(
|
44 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3f4dc464a0ea.png?raw=true',
|
45 |
+
os.path.join(cache_dir, 'examples/3f4dc464a0ea.jpg')), None,
|
46 |
+
None, '{image} let the man serious', 99999
|
47 |
+
],
|
48 |
+
[
|
49 |
+
'Controllable Generation',
|
50 |
+
download_image(
|
51 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/131ca90fd2a9.png?raw=true',
|
52 |
+
os.path.join(cache_dir,
|
53 |
+
'examples/131ca90fd2a9.jpg')), None, None,
|
54 |
+
'"A person sits contemplatively on the ground, surrounded by falling autumn leaves. Dressed in a green sweater and dark blue pants, they rest their chin on their hand, exuding a relaxed demeanor. Their stylish checkered slip-on shoes add a touch of flair, while a black purse lies in their lap. The backdrop of muted brown enhances the warm, cozy atmosphere of the scene." , generate the image that corresponds to the given scribble {image}.',
|
55 |
+
613725
|
56 |
+
],
|
57 |
+
[
|
58 |
+
'Render Text',
|
59 |
+
download_image(
|
60 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/33e9f27c2c48.png?raw=true',
|
61 |
+
os.path.join(cache_dir, 'examples/33e9f27c2c48.jpg')),
|
62 |
+
download_image(
|
63 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/33e9f27c2c48_mask.png?raw=true',
|
64 |
+
os.path.join(cache_dir,
|
65 |
+
'examples/33e9f27c2c48_mask.jpg')), None,
|
66 |
+
'Put the text "C A T" at the position marked by mask in the {image}',
|
67 |
+
6666
|
68 |
+
],
|
69 |
+
[
|
70 |
+
'Style Transfer',
|
71 |
+
download_image(
|
72 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/9e73e7eeef55.png?raw=true',
|
73 |
+
os.path.join(cache_dir, 'examples/9e73e7eeef55.jpg')), None,
|
74 |
+
download_image(
|
75 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/2e02975293d6.png?raw=true',
|
76 |
+
os.path.join(cache_dir, 'examples/2e02975293d6.jpg')),
|
77 |
+
'edit {image} based on the style of {image1} ', 99999
|
78 |
+
],
|
79 |
+
[
|
80 |
+
'Outpainting',
|
81 |
+
download_image(
|
82 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f2b22c08be3f.png?raw=true',
|
83 |
+
os.path.join(cache_dir, 'examples/f2b22c08be3f.jpg')),
|
84 |
+
download_image(
|
85 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f2b22c08be3f_mask.png?raw=true',
|
86 |
+
os.path.join(cache_dir,
|
87 |
+
'examples/f2b22c08be3f_mask.jpg')), None,
|
88 |
+
'Could the {image} be widened within the space designated by mask, while retaining the original?',
|
89 |
+
6666
|
90 |
+
],
|
91 |
+
[
|
92 |
+
'Image Segmentation',
|
93 |
+
download_image(
|
94 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/db3ebaa81899.png?raw=true',
|
95 |
+
os.path.join(cache_dir, 'examples/db3ebaa81899.jpg')), None,
|
96 |
+
None, '{image} Segmentation', 6666
|
97 |
+
],
|
98 |
+
[
|
99 |
+
'Depth Estimation',
|
100 |
+
download_image(
|
101 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/f1927c4692ba.png?raw=true',
|
102 |
+
os.path.join(cache_dir, 'examples/f1927c4692ba.jpg')), None,
|
103 |
+
None, '{image} Depth Estimation', 6666
|
104 |
+
],
|
105 |
+
[
|
106 |
+
'Pose Estimation',
|
107 |
+
download_image(
|
108 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/014e5bf3b4d1.png?raw=true',
|
109 |
+
os.path.join(cache_dir, 'examples/014e5bf3b4d1.jpg')), None,
|
110 |
+
None, '{image} distinguish the poses of the figures', 999999
|
111 |
+
],
|
112 |
+
[
|
113 |
+
'Scribble Extraction',
|
114 |
+
download_image(
|
115 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/5f59a202f8ac.png?raw=true',
|
116 |
+
os.path.join(cache_dir, 'examples/5f59a202f8ac.jpg')), None,
|
117 |
+
None, 'Generate a scribble of {image}, please.', 6666
|
118 |
+
],
|
119 |
+
[
|
120 |
+
'Mosaic',
|
121 |
+
download_image(
|
122 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3a2f52361eea.png?raw=true',
|
123 |
+
os.path.join(cache_dir, 'examples/3a2f52361eea.jpg')), None,
|
124 |
+
None, 'Adapt {image} into a mosaic representation.', 6666
|
125 |
+
],
|
126 |
+
[
|
127 |
+
'Edge map Extraction',
|
128 |
+
download_image(
|
129 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/b9d1e519d6e5.png?raw=true',
|
130 |
+
os.path.join(cache_dir, 'examples/b9d1e519d6e5.jpg')), None,
|
131 |
+
None, 'Get the edge-enhanced result for {image}.', 6666
|
132 |
+
],
|
133 |
+
[
|
134 |
+
'Grayscale',
|
135 |
+
download_image(
|
136 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4ebbe2ba29b.png?raw=true',
|
137 |
+
os.path.join(cache_dir, 'examples/c4ebbe2ba29b.jpg')), None,
|
138 |
+
None, 'transform {image} into a black and white one', 6666
|
139 |
+
],
|
140 |
+
[
|
141 |
+
'Contour Extraction',
|
142 |
+
download_image(
|
143 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/19652d0f6c4b.png?raw=true',
|
144 |
+
os.path.join(cache_dir,
|
145 |
+
'examples/19652d0f6c4b.jpg')), None, None,
|
146 |
+
'Would you be able to make a contour picture from {image} for me?',
|
147 |
+
6666
|
148 |
+
],
|
149 |
+
[
|
150 |
+
'Controllable Generation',
|
151 |
+
download_image(
|
152 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/249cda2844b7.png?raw=true',
|
153 |
+
os.path.join(cache_dir,
|
154 |
+
'examples/249cda2844b7.jpg')), None, None,
|
155 |
+
'Following the segmentation outcome in mask of {image}, develop a real-life image using the explanatory note in "a mighty cat lying on the bed”.',
|
156 |
+
6666
|
157 |
+
],
|
158 |
+
[
|
159 |
+
'Controllable Generation',
|
160 |
+
download_image(
|
161 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/411f6c4b8e6c.png?raw=true',
|
162 |
+
os.path.join(cache_dir,
|
163 |
+
'examples/411f6c4b8e6c.jpg')), None, None,
|
164 |
+
'use the depth map {image} and the text caption "a cut white cat" to create a corresponding graphic image',
|
165 |
+
999999
|
166 |
+
],
|
167 |
+
[
|
168 |
+
'Controllable Generation',
|
169 |
+
download_image(
|
170 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/a35c96ed137a.png?raw=true',
|
171 |
+
os.path.join(cache_dir,
|
172 |
+
'examples/a35c96ed137a.jpg')), None, None,
|
173 |
+
'help translate this posture schema {image} into a colored image based on the context I provided "A beautiful woman Climbing the climbing wall, wearing a harness and climbing gear, skillfully maneuvering up the wall with her back to the camera, with a safety rope."',
|
174 |
+
3599999
|
175 |
+
],
|
176 |
+
[
|
177 |
+
'Controllable Generation',
|
178 |
+
download_image(
|
179 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/dcb2fc86f1ce.png?raw=true',
|
180 |
+
os.path.join(cache_dir,
|
181 |
+
'examples/dcb2fc86f1ce.jpg')), None, None,
|
182 |
+
'Transform and generate an image using mosaic {image} and "Monarch butterflies gracefully perch on vibrant purple flowers, showcasing their striking orange and black wings in a lush garden setting." description',
|
183 |
+
6666
|
184 |
+
],
|
185 |
+
[
|
186 |
+
'Controllable Generation',
|
187 |
+
download_image(
|
188 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/4cd4ee494962.png?raw=true',
|
189 |
+
os.path.join(cache_dir,
|
190 |
+
'examples/4cd4ee494962.jpg')), None, None,
|
191 |
+
'make this {image} colorful as per the "beautiful sunflowers"',
|
192 |
+
6666
|
193 |
+
],
|
194 |
+
[
|
195 |
+
'Controllable Generation',
|
196 |
+
download_image(
|
197 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/a47e3a9cd166.png?raw=true',
|
198 |
+
os.path.join(cache_dir,
|
199 |
+
'examples/a47e3a9cd166.jpg')), None, None,
|
200 |
+
'Take the edge conscious {image} and the written guideline "A whimsical animated character is depicted holding a delectable cake adorned with blue and white frosting and a drizzle of chocolate. The character wears a yellow headband with a bow, matching a cozy yellow sweater. Her dark hair is styled in a braid, tied with a yellow ribbon. With a golden fork in hand, she stands ready to enjoy a slice, exuding an air of joyful anticipation. The scene is creatively rendered with a charming and playful aesthetic." and produce a realistic image.',
|
201 |
+
613725
|
202 |
+
],
|
203 |
+
[
|
204 |
+
'Controllable Generation',
|
205 |
+
download_image(
|
206 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/d890ed8a3ac2.png?raw=true',
|
207 |
+
os.path.join(cache_dir,
|
208 |
+
'examples/d890ed8a3ac2.jpg')), None, None,
|
209 |
+
'creating a vivid image based on {image} and description "This image features a delicious rectangular tart with a flaky, golden-brown crust. The tart is topped with evenly sliced tomatoes, layered over a creamy cheese filling. Aromatic herbs are sprinkled on top, adding a touch of green and enhancing the visual appeal. The background includes a soft, textured fabric and scattered white flowers, creating an elegant and inviting presentation. Bright red tomatoes in the upper right corner hint at the fresh ingredients used in the dish."',
|
210 |
+
6666
|
211 |
+
],
|
212 |
+
[
|
213 |
+
'Image Denoising',
|
214 |
+
download_image(
|
215 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/0844a686a179.png?raw=true',
|
216 |
+
os.path.join(cache_dir,
|
217 |
+
'examples/0844a686a179.jpg')), None, None,
|
218 |
+
'Eliminate noise interference in {image} and maximize the crispness to obtain superior high-definition quality',
|
219 |
+
6666
|
220 |
+
],
|
221 |
+
[
|
222 |
+
'Inpainting',
|
223 |
+
download_image(
|
224 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/fa91b6b7e59b.png?raw=true',
|
225 |
+
os.path.join(cache_dir, 'examples/fa91b6b7e59b.jpg')),
|
226 |
+
download_image(
|
227 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/fa91b6b7e59b_mask.png?raw=true',
|
228 |
+
os.path.join(cache_dir,
|
229 |
+
'examples/fa91b6b7e59b_mask.jpg')), None,
|
230 |
+
'Ensure to overhaul the parts of the {image} indicated by the mask.',
|
231 |
+
6666
|
232 |
+
],
|
233 |
+
[
|
234 |
+
'Inpainting',
|
235 |
+
download_image(
|
236 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/632899695b26.png?raw=true',
|
237 |
+
os.path.join(cache_dir, 'examples/632899695b26.jpg')),
|
238 |
+
download_image(
|
239 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/632899695b26_mask.png?raw=true',
|
240 |
+
os.path.join(cache_dir,
|
241 |
+
'examples/632899695b26_mask.jpg')), None,
|
242 |
+
'Refashion the mask portion of {image} in accordance with "A yellow egg with a smiling face painted on it"',
|
243 |
+
6666
|
244 |
+
],
|
245 |
+
[
|
246 |
+
'General Editing',
|
247 |
+
download_image(
|
248 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/354d17594afe.png?raw=true',
|
249 |
+
os.path.join(cache_dir,
|
250 |
+
'examples/354d17594afe.jpg')), None, None,
|
251 |
+
'{image} change the dog\'s posture to walking in the water, and change the background to green plants and a pond.',
|
252 |
+
6666
|
253 |
+
],
|
254 |
+
[
|
255 |
+
'General Editing',
|
256 |
+
download_image(
|
257 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/38946455752b.png?raw=true',
|
258 |
+
os.path.join(cache_dir,
|
259 |
+
'examples/38946455752b.jpg')), None, None,
|
260 |
+
'{image} change the color of the dress from white to red and the model\'s hair color red brown to blonde.Other parts remain unchanged',
|
261 |
+
6669
|
262 |
+
],
|
263 |
+
[
|
264 |
+
'Facial Editing',
|
265 |
+
download_image(
|
266 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/3ba5202f0cd8.png?raw=true',
|
267 |
+
os.path.join(cache_dir,
|
268 |
+
'examples/3ba5202f0cd8.jpg')), None, None,
|
269 |
+
'Keep the same facial feature in @3ba5202f0cd8, change the woman\'s clothing from a Blue denim jacket to a white turtleneck sweater and adjust her posture so that she is supporting her chin with both hands. Other aspects, such as background, hairstyle, facial expression, etc, remain unchanged.',
|
270 |
+
99999
|
271 |
+
],
|
272 |
+
[
|
273 |
+
'Facial Editing',
|
274 |
+
download_image(
|
275 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/369365b94725.png?raw=true',
|
276 |
+
os.path.join(cache_dir, 'examples/369365b94725.jpg')), None,
|
277 |
+
None, '{image} Make her looking at the camera', 6666
|
278 |
+
],
|
279 |
+
[
|
280 |
+
'Facial Editing',
|
281 |
+
download_image(
|
282 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/92751f2e4a0e.png?raw=true',
|
283 |
+
os.path.join(cache_dir, 'examples/92751f2e4a0e.jpg')), None,
|
284 |
+
None, '{image} Remove the smile from his face', 9899999
|
285 |
+
],
|
286 |
+
[
|
287 |
+
'Remove Text',
|
288 |
+
download_image(
|
289 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/8530a6711b2e.png?raw=true',
|
290 |
+
os.path.join(cache_dir, 'examples/8530a6711b2e.jpg')), None,
|
291 |
+
None, 'Aim to remove any textual element in {image}', 6666
|
292 |
+
],
|
293 |
+
[
|
294 |
+
'Remove Text',
|
295 |
+
download_image(
|
296 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4d7fb28f8f6.png?raw=true',
|
297 |
+
os.path.join(cache_dir, 'examples/c4d7fb28f8f6.jpg')),
|
298 |
+
download_image(
|
299 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/c4d7fb28f8f6_mask.png?raw=true',
|
300 |
+
os.path.join(cache_dir,
|
301 |
+
'examples/c4d7fb28f8f6_mask.jpg')), None,
|
302 |
+
'Rub out any text found in the mask sector of the {image}.', 6666
|
303 |
+
],
|
304 |
+
[
|
305 |
+
'Remove Object',
|
306 |
+
download_image(
|
307 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e2f318fa5e5b.png?raw=true',
|
308 |
+
os.path.join(cache_dir,
|
309 |
+
'examples/e2f318fa5e5b.jpg')), None, None,
|
310 |
+
'Remove the unicorn in this {image}, ensuring a smooth edit.',
|
311 |
+
99999
|
312 |
+
],
|
313 |
+
[
|
314 |
+
'Remove Object',
|
315 |
+
download_image(
|
316 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/1ae96d8aca00.png?raw=true',
|
317 |
+
os.path.join(cache_dir, 'examples/1ae96d8aca00.jpg')),
|
318 |
+
download_image(
|
319 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/1ae96d8aca00_mask.png?raw=true',
|
320 |
+
os.path.join(cache_dir, 'examples/1ae96d8aca00_mask.jpg')),
|
321 |
+
None, 'Discard the contents of the mask area from {image}.', 99999
|
322 |
+
],
|
323 |
+
[
|
324 |
+
'Add Object',
|
325 |
+
download_image(
|
326 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/80289f48e511.png?raw=true',
|
327 |
+
os.path.join(cache_dir, 'examples/80289f48e511.jpg')),
|
328 |
+
download_image(
|
329 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/80289f48e511_mask.png?raw=true',
|
330 |
+
os.path.join(cache_dir,
|
331 |
+
'examples/80289f48e511_mask.jpg')), None,
|
332 |
+
'add a Hot Air Balloon into the {image}, per the mask', 613725
|
333 |
+
],
|
334 |
+
[
|
335 |
+
'Style Transfer',
|
336 |
+
download_image(
|
337 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/d725cb2009e8.png?raw=true',
|
338 |
+
os.path.join(cache_dir, 'examples/d725cb2009e8.jpg')), None,
|
339 |
+
None, 'Change the style of {image} to colored pencil style', 99999
|
340 |
+
],
|
341 |
+
[
|
342 |
+
'Style Transfer',
|
343 |
+
download_image(
|
344 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/e0f48b3fd010.png?raw=true',
|
345 |
+
os.path.join(cache_dir, 'examples/e0f48b3fd010.jpg')), None,
|
346 |
+
None, 'make {image} to Walt Disney Animation style', 99999
|
347 |
+
],
|
348 |
+
[
|
349 |
+
'Try On',
|
350 |
+
download_image(
|
351 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/ee4ca60b8c96.png?raw=true',
|
352 |
+
os.path.join(cache_dir, 'examples/ee4ca60b8c96.jpg')),
|
353 |
+
download_image(
|
354 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/ee4ca60b8c96_mask.png?raw=true',
|
355 |
+
os.path.join(cache_dir, 'examples/ee4ca60b8c96_mask.jpg')),
|
356 |
+
download_image(
|
357 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/ebe825bbfe3c.png?raw=true',
|
358 |
+
os.path.join(cache_dir, 'examples/ebe825bbfe3c.jpg')),
|
359 |
+
'Change the cloth in {image} to the one in {image1}', 99999
|
360 |
+
],
|
361 |
+
[
|
362 |
+
'Workflow',
|
363 |
+
download_image(
|
364 |
+
'https://github.com/ali-vilab/ace-page/blob/main/assets/examples/cb85353c004b.png?raw=true',
|
365 |
+
os.path.join(cache_dir, 'examples/cb85353c004b.jpg')), None,
|
366 |
+
None, '<workflow> ice cream {image}', 99999
|
367 |
+
],
|
368 |
+
]
|
369 |
+
print('Finish. Start building UI ...')
|
370 |
+
return examples
|
model/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
#from .flux import Flux, FluxMR, FluxEdit
|
model/flux.py
ADDED
@@ -0,0 +1,1064 @@
|
|
|
|
|
|
|
|
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|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
# Copyright (c) Alibaba, Inc. and its affiliates.
|
3 |
+
import math
|
4 |
+
from collections import OrderedDict
|
5 |
+
from functools import partial
|
6 |
+
import warnings
|
7 |
+
from contextlib import nullcontext
|
8 |
+
import torch
|
9 |
+
from einops import rearrange, repeat
|
10 |
+
from scepter.modules.model.base_model import BaseModel
|
11 |
+
from scepter.modules.model.registry import BACKBONES
|
12 |
+
from scepter.modules.utils.config import dict_to_yaml
|
13 |
+
from scepter.modules.utils.distribute import we
|
14 |
+
from scepter.modules.utils.file_system import FS
|
15 |
+
from torch import Tensor, nn
|
16 |
+
from torch.nn.utils.rnn import pad_sequence
|
17 |
+
from torch.utils.checkpoint import checkpoint_sequential
|
18 |
+
import torch.nn.functional as F
|
19 |
+
import torch.utils.dlpack
|
20 |
+
import transformers
|
21 |
+
from scepter.modules.model.embedder.base_embedder import BaseEmbedder
|
22 |
+
from scepter.modules.model.registry import EMBEDDERS
|
23 |
+
from scepter.modules.model.tokenizer.tokenizer_component import (
|
24 |
+
basic_clean, canonicalize, heavy_clean, whitespace_clean)
|
25 |
+
try:
|
26 |
+
from transformers import AutoTokenizer, T5EncoderModel
|
27 |
+
except Exception as e:
|
28 |
+
warnings.warn(
|
29 |
+
f'Import transformers error, please deal with this problem: {e}')
|
30 |
+
|
31 |
+
from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
|
32 |
+
MLPEmbedder, SingleStreamBlock,
|
33 |
+
timestep_embedding)
|
34 |
+
|
35 |
+
|
36 |
+
|
37 |
+
@EMBEDDERS.register_class()
|
38 |
+
class ACETextEmbedder(BaseEmbedder):
|
39 |
+
"""
|
40 |
+
Uses the OpenCLIP transformer encoder for text
|
41 |
+
"""
|
42 |
+
"""
|
43 |
+
Uses the OpenCLIP transformer encoder for text
|
44 |
+
"""
|
45 |
+
para_dict = {
|
46 |
+
'PRETRAINED_MODEL': {
|
47 |
+
'value':
|
48 |
+
'google/umt5-small',
|
49 |
+
'description':
|
50 |
+
'Pretrained Model for umt5, modelcard path or local path.'
|
51 |
+
},
|
52 |
+
'TOKENIZER_PATH': {
|
53 |
+
'value': 'google/umt5-small',
|
54 |
+
'description':
|
55 |
+
'Tokenizer Path for umt5, modelcard path or local path.'
|
56 |
+
},
|
57 |
+
'FREEZE': {
|
58 |
+
'value': True,
|
59 |
+
'description': ''
|
60 |
+
},
|
61 |
+
'USE_GRAD': {
|
62 |
+
'value': False,
|
63 |
+
'description': 'Compute grad or not.'
|
64 |
+
},
|
65 |
+
'CLEAN': {
|
66 |
+
'value':
|
67 |
+
'whitespace',
|
68 |
+
'description':
|
69 |
+
'Set the clean strtegy for tokenizer, used when TOKENIZER_PATH is not None.'
|
70 |
+
},
|
71 |
+
'LAYER': {
|
72 |
+
'value': 'last',
|
73 |
+
'description': ''
|
74 |
+
},
|
75 |
+
'LEGACY': {
|
76 |
+
'value':
|
77 |
+
True,
|
78 |
+
'description':
|
79 |
+
'Whether use legacy returnd feature or not ,default True.'
|
80 |
+
}
|
81 |
+
}
|
82 |
+
|
83 |
+
def __init__(self, cfg, logger=None):
|
84 |
+
super().__init__(cfg, logger=logger)
|
85 |
+
pretrained_path = cfg.get('PRETRAINED_MODEL', None)
|
86 |
+
self.t5_dtype = cfg.get('T5_DTYPE', 'float32')
|
87 |
+
assert pretrained_path
|
88 |
+
with FS.get_dir_to_local_dir(pretrained_path,
|
89 |
+
wait_finish=True) as local_path:
|
90 |
+
self.model = T5EncoderModel.from_pretrained(
|
91 |
+
local_path,
|
92 |
+
torch_dtype=getattr(
|
93 |
+
torch,
|
94 |
+
'float' if self.t5_dtype == 'float32' else self.t5_dtype))
|
95 |
+
tokenizer_path = cfg.get('TOKENIZER_PATH', None)
|
96 |
+
self.length = cfg.get('LENGTH', 77)
|
97 |
+
|
98 |
+
self.use_grad = cfg.get('USE_GRAD', False)
|
99 |
+
self.clean = cfg.get('CLEAN', 'whitespace')
|
100 |
+
self.added_identifier = cfg.get('ADDED_IDENTIFIER', None)
|
101 |
+
if tokenizer_path:
|
102 |
+
self.tokenize_kargs = {'return_tensors': 'pt'}
|
103 |
+
with FS.get_dir_to_local_dir(tokenizer_path,
|
104 |
+
wait_finish=True) as local_path:
|
105 |
+
if self.added_identifier is not None and isinstance(
|
106 |
+
self.added_identifier, list):
|
107 |
+
self.tokenizer = AutoTokenizer.from_pretrained(local_path)
|
108 |
+
else:
|
109 |
+
self.tokenizer = AutoTokenizer.from_pretrained(local_path)
|
110 |
+
if self.length is not None:
|
111 |
+
self.tokenize_kargs.update({
|
112 |
+
'padding': 'max_length',
|
113 |
+
'truncation': True,
|
114 |
+
'max_length': self.length
|
115 |
+
})
|
116 |
+
self.eos_token = self.tokenizer(
|
117 |
+
self.tokenizer.eos_token)['input_ids'][0]
|
118 |
+
else:
|
119 |
+
self.tokenizer = None
|
120 |
+
self.tokenize_kargs = {}
|
121 |
+
|
122 |
+
self.use_grad = cfg.get('USE_GRAD', False)
|
123 |
+
self.clean = cfg.get('CLEAN', 'whitespace')
|
124 |
+
|
125 |
+
def freeze(self):
|
126 |
+
self.model = self.model.eval()
|
127 |
+
for param in self.parameters():
|
128 |
+
param.requires_grad = False
|
129 |
+
|
130 |
+
# encode && encode_text
|
131 |
+
def forward(self, tokens, return_mask=False, use_mask=True):
|
132 |
+
# tokenization
|
133 |
+
embedding_context = nullcontext if self.use_grad else torch.no_grad
|
134 |
+
with embedding_context():
|
135 |
+
if use_mask:
|
136 |
+
x = self.model(tokens.input_ids.to(we.device_id),
|
137 |
+
tokens.attention_mask.to(we.device_id))
|
138 |
+
else:
|
139 |
+
x = self.model(tokens.input_ids.to(we.device_id))
|
140 |
+
x = x.last_hidden_state
|
141 |
+
|
142 |
+
if return_mask:
|
143 |
+
return x.detach() + 0.0, tokens.attention_mask.to(we.device_id)
|
144 |
+
else:
|
145 |
+
return x.detach() + 0.0, None
|
146 |
+
|
147 |
+
def _clean(self, text):
|
148 |
+
if self.clean == 'whitespace':
|
149 |
+
text = whitespace_clean(basic_clean(text))
|
150 |
+
elif self.clean == 'lower':
|
151 |
+
text = whitespace_clean(basic_clean(text)).lower()
|
152 |
+
elif self.clean == 'canonicalize':
|
153 |
+
text = canonicalize(basic_clean(text))
|
154 |
+
elif self.clean == 'heavy':
|
155 |
+
text = heavy_clean(basic_clean(text))
|
156 |
+
return text
|
157 |
+
|
158 |
+
def encode(self, text, return_mask=False, use_mask=True):
|
159 |
+
if isinstance(text, str):
|
160 |
+
text = [text]
|
161 |
+
if self.clean:
|
162 |
+
text = [self._clean(u) for u in text]
|
163 |
+
assert self.tokenizer is not None
|
164 |
+
cont, mask = [], []
|
165 |
+
with torch.autocast(device_type='cuda',
|
166 |
+
enabled=self.t5_dtype in ('float16', 'bfloat16'),
|
167 |
+
dtype=getattr(torch, self.t5_dtype)):
|
168 |
+
for tt in text:
|
169 |
+
tokens = self.tokenizer([tt], **self.tokenize_kargs)
|
170 |
+
one_cont, one_mask = self(tokens,
|
171 |
+
return_mask=return_mask,
|
172 |
+
use_mask=use_mask)
|
173 |
+
cont.append(one_cont)
|
174 |
+
mask.append(one_mask)
|
175 |
+
if return_mask:
|
176 |
+
return torch.cat(cont, dim=0), torch.cat(mask, dim=0)
|
177 |
+
else:
|
178 |
+
return torch.cat(cont, dim=0)
|
179 |
+
|
180 |
+
def encode_list(self, text_list, return_mask=True):
|
181 |
+
cont_list = []
|
182 |
+
mask_list = []
|
183 |
+
for pp in text_list:
|
184 |
+
cont, cont_mask = self.encode(pp, return_mask=return_mask)
|
185 |
+
cont_list.append(cont)
|
186 |
+
mask_list.append(cont_mask)
|
187 |
+
if return_mask:
|
188 |
+
return cont_list, mask_list
|
189 |
+
else:
|
190 |
+
return cont_list
|
191 |
+
|
192 |
+
@staticmethod
|
193 |
+
def get_config_template():
|
194 |
+
return dict_to_yaml('MODELS',
|
195 |
+
__class__.__name__,
|
196 |
+
ACETextEmbedder.para_dict,
|
197 |
+
set_name=True)
|
198 |
+
|
199 |
+
@EMBEDDERS.register_class()
|
200 |
+
class ACEHFEmbedder(BaseEmbedder):
|
201 |
+
para_dict = {
|
202 |
+
"HF_MODEL_CLS": {
|
203 |
+
"value": None,
|
204 |
+
"description": "huggingface cls in transfomer"
|
205 |
+
},
|
206 |
+
"MODEL_PATH": {
|
207 |
+
"value": None,
|
208 |
+
"description": "model folder path"
|
209 |
+
},
|
210 |
+
"HF_TOKENIZER_CLS": {
|
211 |
+
"value": None,
|
212 |
+
"description": "huggingface cls in transfomer"
|
213 |
+
},
|
214 |
+
|
215 |
+
"TOKENIZER_PATH": {
|
216 |
+
"value": None,
|
217 |
+
"description": "tokenizer folder path"
|
218 |
+
},
|
219 |
+
"MAX_LENGTH": {
|
220 |
+
"value": 77,
|
221 |
+
"description": "max length of input"
|
222 |
+
},
|
223 |
+
"OUTPUT_KEY": {
|
224 |
+
"value": "last_hidden_state",
|
225 |
+
"description": "output key"
|
226 |
+
},
|
227 |
+
"D_TYPE": {
|
228 |
+
"value": "float",
|
229 |
+
"description": "dtype"
|
230 |
+
},
|
231 |
+
"BATCH_INFER": {
|
232 |
+
"value": False,
|
233 |
+
"description": "batch infer"
|
234 |
+
}
|
235 |
+
}
|
236 |
+
para_dict.update(BaseEmbedder.para_dict)
|
237 |
+
def __init__(self, cfg, logger=None):
|
238 |
+
super().__init__(cfg, logger=logger)
|
239 |
+
hf_model_cls = cfg.get('HF_MODEL_CLS', None)
|
240 |
+
model_path = cfg.get("MODEL_PATH", None)
|
241 |
+
hf_tokenizer_cls = cfg.get('HF_TOKENIZER_CLS', None)
|
242 |
+
tokenizer_path = cfg.get('TOKENIZER_PATH', None)
|
243 |
+
self.max_length = cfg.get('MAX_LENGTH', 77)
|
244 |
+
self.output_key = cfg.get("OUTPUT_KEY", "last_hidden_state")
|
245 |
+
self.d_type = cfg.get("D_TYPE", "float")
|
246 |
+
self.clean = cfg.get("CLEAN", "whitespace")
|
247 |
+
self.batch_infer = cfg.get("BATCH_INFER", False)
|
248 |
+
self.added_identifier = cfg.get('ADDED_IDENTIFIER', None)
|
249 |
+
torch_dtype = getattr(torch, self.d_type)
|
250 |
+
|
251 |
+
assert hf_model_cls is not None and hf_tokenizer_cls is not None
|
252 |
+
assert model_path is not None and tokenizer_path is not None
|
253 |
+
with FS.get_dir_to_local_dir(tokenizer_path, wait_finish=True) as local_path:
|
254 |
+
self.tokenizer = getattr(transformers, hf_tokenizer_cls).from_pretrained(local_path,
|
255 |
+
max_length = self.max_length,
|
256 |
+
torch_dtype = torch_dtype,
|
257 |
+
additional_special_tokens=self.added_identifier)
|
258 |
+
|
259 |
+
with FS.get_dir_to_local_dir(model_path, wait_finish=True) as local_path:
|
260 |
+
self.hf_module = getattr(transformers, hf_model_cls).from_pretrained(local_path, torch_dtype = torch_dtype)
|
261 |
+
|
262 |
+
|
263 |
+
self.hf_module = self.hf_module.eval().requires_grad_(False)
|
264 |
+
|
265 |
+
def forward(self, text: list[str], return_mask = False):
|
266 |
+
batch_encoding = self.tokenizer(
|
267 |
+
text,
|
268 |
+
truncation=True,
|
269 |
+
max_length=self.max_length,
|
270 |
+
return_length=False,
|
271 |
+
return_overflowing_tokens=False,
|
272 |
+
padding="max_length",
|
273 |
+
return_tensors="pt",
|
274 |
+
)
|
275 |
+
|
276 |
+
outputs = self.hf_module(
|
277 |
+
input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
|
278 |
+
attention_mask=None,
|
279 |
+
output_hidden_states=False,
|
280 |
+
)
|
281 |
+
if return_mask:
|
282 |
+
return outputs[self.output_key], batch_encoding['attention_mask'].to(self.hf_module.device)
|
283 |
+
else:
|
284 |
+
return outputs[self.output_key], None
|
285 |
+
|
286 |
+
def encode(self, text, return_mask = False):
|
287 |
+
if isinstance(text, str):
|
288 |
+
text = [text]
|
289 |
+
if self.clean:
|
290 |
+
text = [self._clean(u) for u in text]
|
291 |
+
if not self.batch_infer:
|
292 |
+
cont, mask = [], []
|
293 |
+
for tt in text:
|
294 |
+
one_cont, one_mask = self([tt], return_mask=return_mask)
|
295 |
+
cont.append(one_cont)
|
296 |
+
mask.append(one_mask)
|
297 |
+
if return_mask:
|
298 |
+
return torch.cat(cont, dim=0), torch.cat(mask, dim=0)
|
299 |
+
else:
|
300 |
+
return torch.cat(cont, dim=0)
|
301 |
+
else:
|
302 |
+
ret_data = self(text, return_mask = return_mask)
|
303 |
+
if return_mask:
|
304 |
+
return ret_data
|
305 |
+
else:
|
306 |
+
return ret_data[0]
|
307 |
+
|
308 |
+
def encode_list(self, text_list, return_mask=True):
|
309 |
+
cont_list = []
|
310 |
+
mask_list = []
|
311 |
+
for pp in text_list:
|
312 |
+
cont = self.encode(pp, return_mask=return_mask)
|
313 |
+
cont_list.append(cont[0]) if return_mask else cont_list.append(cont)
|
314 |
+
mask_list.append(cont[1]) if return_mask else mask_list.append(None)
|
315 |
+
if return_mask:
|
316 |
+
return cont_list, mask_list
|
317 |
+
else:
|
318 |
+
return cont_list
|
319 |
+
|
320 |
+
def encode_list_of_list(self, text_list, return_mask=True):
|
321 |
+
cont_list = []
|
322 |
+
mask_list = []
|
323 |
+
for pp in text_list:
|
324 |
+
cont = self.encode_list(pp, return_mask=return_mask)
|
325 |
+
cont_list.append(cont[0]) if return_mask else cont_list.append(cont)
|
326 |
+
mask_list.append(cont[1]) if return_mask else mask_list.append(None)
|
327 |
+
if return_mask:
|
328 |
+
return cont_list, mask_list
|
329 |
+
else:
|
330 |
+
return cont_list
|
331 |
+
|
332 |
+
def _clean(self, text):
|
333 |
+
if self.clean == 'whitespace':
|
334 |
+
text = whitespace_clean(basic_clean(text))
|
335 |
+
elif self.clean == 'lower':
|
336 |
+
text = whitespace_clean(basic_clean(text)).lower()
|
337 |
+
elif self.clean == 'canonicalize':
|
338 |
+
text = canonicalize(basic_clean(text))
|
339 |
+
return text
|
340 |
+
@staticmethod
|
341 |
+
def get_config_template():
|
342 |
+
return dict_to_yaml('EMBEDDER',
|
343 |
+
__class__.__name__,
|
344 |
+
ACEHFEmbedder.para_dict,
|
345 |
+
set_name=True)
|
346 |
+
|
347 |
+
@EMBEDDERS.register_class()
|
348 |
+
class T5ACEPlusClipFluxEmbedder(BaseEmbedder):
|
349 |
+
"""
|
350 |
+
Uses the OpenCLIP transformer encoder for text
|
351 |
+
"""
|
352 |
+
para_dict = {
|
353 |
+
'T5_MODEL': {},
|
354 |
+
'CLIP_MODEL': {}
|
355 |
+
}
|
356 |
+
|
357 |
+
def __init__(self, cfg, logger=None):
|
358 |
+
super().__init__(cfg, logger=logger)
|
359 |
+
self.t5_model = EMBEDDERS.build(cfg.T5_MODEL, logger=logger)
|
360 |
+
self.clip_model = EMBEDDERS.build(cfg.CLIP_MODEL, logger=logger)
|
361 |
+
|
362 |
+
def encode(self, text, return_mask = False):
|
363 |
+
t5_embeds = self.t5_model.encode(text, return_mask = return_mask)
|
364 |
+
clip_embeds = self.clip_model.encode(text, return_mask = return_mask)
|
365 |
+
# change embedding strategy here
|
366 |
+
return {
|
367 |
+
'context': t5_embeds,
|
368 |
+
'y': clip_embeds,
|
369 |
+
}
|
370 |
+
|
371 |
+
def encode_list(self, text, return_mask = False):
|
372 |
+
t5_embeds = self.t5_model.encode_list(text, return_mask = return_mask)
|
373 |
+
clip_embeds = self.clip_model.encode_list(text, return_mask = return_mask)
|
374 |
+
# change embedding strategy here
|
375 |
+
return {
|
376 |
+
'context': t5_embeds,
|
377 |
+
'y': clip_embeds,
|
378 |
+
}
|
379 |
+
|
380 |
+
def encode_list_of_list(self, text, return_mask = False):
|
381 |
+
t5_embeds = self.t5_model.encode_list_of_list(text, return_mask = return_mask)
|
382 |
+
clip_embeds = self.clip_model.encode_list_of_list(text, return_mask = return_mask)
|
383 |
+
# change embedding strategy here
|
384 |
+
return {
|
385 |
+
'context': t5_embeds,
|
386 |
+
'y': clip_embeds,
|
387 |
+
}
|
388 |
+
|
389 |
+
|
390 |
+
@staticmethod
|
391 |
+
def get_config_template():
|
392 |
+
return dict_to_yaml('EMBEDDER',
|
393 |
+
__class__.__name__,
|
394 |
+
T5ACEPlusClipFluxEmbedder.para_dict,
|
395 |
+
set_name=True)
|
396 |
+
|
397 |
+
@BACKBONES.register_class()
|
398 |
+
class Flux(BaseModel):
|
399 |
+
"""
|
400 |
+
Transformer backbone Diffusion model with RoPE.
|
401 |
+
"""
|
402 |
+
para_dict = {
|
403 |
+
"IN_CHANNELS": {
|
404 |
+
"value": 64,
|
405 |
+
"description": "model's input channels."
|
406 |
+
},
|
407 |
+
"OUT_CHANNELS": {
|
408 |
+
"value": 64,
|
409 |
+
"description": "model's output channels."
|
410 |
+
},
|
411 |
+
"HIDDEN_SIZE": {
|
412 |
+
"value": 1024,
|
413 |
+
"description": "model's hidden size."
|
414 |
+
},
|
415 |
+
"NUM_HEADS": {
|
416 |
+
"value": 16,
|
417 |
+
"description": "number of heads in the transformer."
|
418 |
+
},
|
419 |
+
"AXES_DIM": {
|
420 |
+
"value": [16, 56, 56],
|
421 |
+
"description": "dimensions of the axes of the positional encoding."
|
422 |
+
},
|
423 |
+
"THETA": {
|
424 |
+
"value": 10_000,
|
425 |
+
"description": "theta for positional encoding."
|
426 |
+
},
|
427 |
+
"VEC_IN_DIM": {
|
428 |
+
"value": 768,
|
429 |
+
"description": "dimension of the vector input."
|
430 |
+
},
|
431 |
+
"GUIDANCE_EMBED": {
|
432 |
+
"value": False,
|
433 |
+
"description": "whether to use guidance embedding."
|
434 |
+
},
|
435 |
+
"CONTEXT_IN_DIM": {
|
436 |
+
"value": 4096,
|
437 |
+
"description": "dimension of the context input."
|
438 |
+
},
|
439 |
+
"MLP_RATIO": {
|
440 |
+
"value": 4.0,
|
441 |
+
"description": "ratio of mlp hidden size to hidden size."
|
442 |
+
},
|
443 |
+
"QKV_BIAS": {
|
444 |
+
"value": True,
|
445 |
+
"description": "whether to use bias in qkv projection."
|
446 |
+
},
|
447 |
+
"DEPTH": {
|
448 |
+
"value": 19,
|
449 |
+
"description": "number of transformer blocks."
|
450 |
+
},
|
451 |
+
"DEPTH_SINGLE_BLOCKS": {
|
452 |
+
"value": 38,
|
453 |
+
"description": "number of transformer blocks in the single stream block."
|
454 |
+
},
|
455 |
+
"USE_GRAD_CHECKPOINT": {
|
456 |
+
"value": False,
|
457 |
+
"description": "whether to use gradient checkpointing."
|
458 |
+
},
|
459 |
+
"ATTN_BACKEND": {
|
460 |
+
"value": "pytorch",
|
461 |
+
"description": "backend for the transformer blocks, 'pytorch' or 'flash_attn'."
|
462 |
+
}
|
463 |
+
}
|
464 |
+
def __init__(
|
465 |
+
self,
|
466 |
+
cfg,
|
467 |
+
logger = None
|
468 |
+
):
|
469 |
+
super().__init__(cfg, logger=logger)
|
470 |
+
self.in_channels = cfg.IN_CHANNELS
|
471 |
+
self.out_channels = cfg.get("OUT_CHANNELS", self.in_channels)
|
472 |
+
hidden_size = cfg.get("HIDDEN_SIZE", 1024)
|
473 |
+
num_heads = cfg.get("NUM_HEADS", 16)
|
474 |
+
axes_dim = cfg.AXES_DIM
|
475 |
+
theta = cfg.THETA
|
476 |
+
vec_in_dim = cfg.VEC_IN_DIM
|
477 |
+
self.guidance_embed = cfg.GUIDANCE_EMBED
|
478 |
+
context_in_dim = cfg.CONTEXT_IN_DIM
|
479 |
+
mlp_ratio = cfg.MLP_RATIO
|
480 |
+
qkv_bias = cfg.QKV_BIAS
|
481 |
+
depth = cfg.DEPTH
|
482 |
+
depth_single_blocks = cfg.DEPTH_SINGLE_BLOCKS
|
483 |
+
self.use_grad_checkpoint = cfg.get("USE_GRAD_CHECKPOINT", False)
|
484 |
+
self.attn_backend = cfg.get("ATTN_BACKEND", "pytorch")
|
485 |
+
self.lora_model = cfg.get("DIFFUSERS_LORA_MODEL", None)
|
486 |
+
self.swift_lora_model = cfg.get("SWIFT_LORA_MODEL", None)
|
487 |
+
self.pretrain_adapter = cfg.get("PRETRAIN_ADAPTER", None)
|
488 |
+
|
489 |
+
if hidden_size % num_heads != 0:
|
490 |
+
raise ValueError(
|
491 |
+
f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}"
|
492 |
+
)
|
493 |
+
pe_dim = hidden_size // num_heads
|
494 |
+
if sum(axes_dim) != pe_dim:
|
495 |
+
raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
|
496 |
+
self.hidden_size = hidden_size
|
497 |
+
self.num_heads = num_heads
|
498 |
+
self.pe_embedder = EmbedND(dim=pe_dim, theta=theta, axes_dim= axes_dim)
|
499 |
+
self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
|
500 |
+
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
|
501 |
+
self.vector_in = MLPEmbedder(vec_in_dim, self.hidden_size)
|
502 |
+
self.guidance_in = (
|
503 |
+
MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if self.guidance_embed else nn.Identity()
|
504 |
+
)
|
505 |
+
self.txt_in = nn.Linear(context_in_dim, self.hidden_size)
|
506 |
+
|
507 |
+
self.double_blocks = nn.ModuleList(
|
508 |
+
[
|
509 |
+
DoubleStreamBlock(
|
510 |
+
self.hidden_size,
|
511 |
+
self.num_heads,
|
512 |
+
mlp_ratio=mlp_ratio,
|
513 |
+
qkv_bias=qkv_bias,
|
514 |
+
backend=self.attn_backend
|
515 |
+
)
|
516 |
+
for _ in range(depth)
|
517 |
+
]
|
518 |
+
)
|
519 |
+
|
520 |
+
self.single_blocks = nn.ModuleList(
|
521 |
+
[
|
522 |
+
SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, backend=self.attn_backend)
|
523 |
+
for _ in range(depth_single_blocks)
|
524 |
+
]
|
525 |
+
)
|
526 |
+
|
527 |
+
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
|
528 |
+
|
529 |
+
def prepare_input(self, x, context, y, x_shape=None):
|
530 |
+
# x.shape [6, 16, 16, 16] target is [6, 16, 768, 1360]
|
531 |
+
bs, c, h, w = x.shape
|
532 |
+
x = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
|
533 |
+
x_id = torch.zeros(h // 2, w // 2, 3)
|
534 |
+
x_id[..., 1] = x_id[..., 1] + torch.arange(h // 2)[:, None]
|
535 |
+
x_id[..., 2] = x_id[..., 2] + torch.arange(w // 2)[None, :]
|
536 |
+
x_ids = repeat(x_id, "h w c -> b (h w) c", b=bs)
|
537 |
+
txt_ids = torch.zeros(bs, context.shape[1], 3)
|
538 |
+
return x, x_ids.to(x), context.to(x), txt_ids.to(x), y.to(x), h, w
|
539 |
+
|
540 |
+
def unpack(self, x: Tensor, height: int, width: int) -> Tensor:
|
541 |
+
return rearrange(
|
542 |
+
x,
|
543 |
+
"b (h w) (c ph pw) -> b c (h ph) (w pw)",
|
544 |
+
h=math.ceil(height/2),
|
545 |
+
w=math.ceil(width/2),
|
546 |
+
ph=2,
|
547 |
+
pw=2,
|
548 |
+
)
|
549 |
+
|
550 |
+
def merge_diffuser_lora(self, ori_sd, lora_sd, scale = 1.0):
|
551 |
+
key_map = {
|
552 |
+
"single_blocks.{}.linear1.weight": {"key_list": [
|
553 |
+
["transformer.single_transformer_blocks.{}.attn.to_q.lora_A.weight",
|
554 |
+
"transformer.single_transformer_blocks.{}.attn.to_q.lora_B.weight"],
|
555 |
+
["transformer.single_transformer_blocks.{}.attn.to_k.lora_A.weight",
|
556 |
+
"transformer.single_transformer_blocks.{}.attn.to_k.lora_B.weight"],
|
557 |
+
["transformer.single_transformer_blocks.{}.attn.to_v.lora_A.weight",
|
558 |
+
"transformer.single_transformer_blocks.{}.attn.to_v.lora_B.weight"],
|
559 |
+
["transformer.single_transformer_blocks.{}.proj_mlp.lora_A.weight",
|
560 |
+
"transformer.single_transformer_blocks.{}.proj_mlp.lora_B.weight"]
|
561 |
+
], "num": 38},
|
562 |
+
"single_blocks.{}.modulation.lin.weight": {"key_list": [
|
563 |
+
["transformer.single_transformer_blocks.{}.norm.linear.lora_A.weight",
|
564 |
+
"transformer.single_transformer_blocks.{}.norm.linear.lora_B.weight"],
|
565 |
+
], "num": 38},
|
566 |
+
"single_blocks.{}.linear2.weight": {"key_list": [
|
567 |
+
["transformer.single_transformer_blocks.{}.proj_out.lora_A.weight",
|
568 |
+
"transformer.single_transformer_blocks.{}.proj_out.lora_B.weight"],
|
569 |
+
], "num": 38},
|
570 |
+
"double_blocks.{}.txt_attn.qkv.weight": {"key_list": [
|
571 |
+
["transformer.transformer_blocks.{}.attn.add_q_proj.lora_A.weight",
|
572 |
+
"transformer.transformer_blocks.{}.attn.add_q_proj.lora_B.weight"],
|
573 |
+
["transformer.transformer_blocks.{}.attn.add_k_proj.lora_A.weight",
|
574 |
+
"transformer.transformer_blocks.{}.attn.add_k_proj.lora_B.weight"],
|
575 |
+
["transformer.transformer_blocks.{}.attn.add_v_proj.lora_A.weight",
|
576 |
+
"transformer.transformer_blocks.{}.attn.add_v_proj.lora_B.weight"],
|
577 |
+
], "num": 19},
|
578 |
+
"double_blocks.{}.img_attn.qkv.weight": {"key_list": [
|
579 |
+
["transformer.transformer_blocks.{}.attn.to_q.lora_A.weight",
|
580 |
+
"transformer.transformer_blocks.{}.attn.to_q.lora_B.weight"],
|
581 |
+
["transformer.transformer_blocks.{}.attn.to_k.lora_A.weight",
|
582 |
+
"transformer.transformer_blocks.{}.attn.to_k.lora_B.weight"],
|
583 |
+
["transformer.transformer_blocks.{}.attn.to_v.lora_A.weight",
|
584 |
+
"transformer.transformer_blocks.{}.attn.to_v.lora_B.weight"],
|
585 |
+
], "num": 19},
|
586 |
+
"double_blocks.{}.img_attn.proj.weight": {"key_list": [
|
587 |
+
["transformer.transformer_blocks.{}.attn.to_out.0.lora_A.weight",
|
588 |
+
"transformer.transformer_blocks.{}.attn.to_out.0.lora_B.weight"]
|
589 |
+
], "num": 19},
|
590 |
+
"double_blocks.{}.txt_attn.proj.weight": {"key_list": [
|
591 |
+
["transformer.transformer_blocks.{}.attn.to_add_out.lora_A.weight",
|
592 |
+
"transformer.transformer_blocks.{}.attn.to_add_out.lora_B.weight"]
|
593 |
+
], "num": 19},
|
594 |
+
"double_blocks.{}.img_mlp.0.weight": {"key_list": [
|
595 |
+
["transformer.transformer_blocks.{}.ff.net.0.proj.lora_A.weight",
|
596 |
+
"transformer.transformer_blocks.{}.ff.net.0.proj.lora_B.weight"]
|
597 |
+
], "num": 19},
|
598 |
+
"double_blocks.{}.img_mlp.2.weight": {"key_list": [
|
599 |
+
["transformer.transformer_blocks.{}.ff.net.2.lora_A.weight",
|
600 |
+
"transformer.transformer_blocks.{}.ff.net.2.lora_B.weight"]
|
601 |
+
], "num": 19},
|
602 |
+
"double_blocks.{}.txt_mlp.0.weight": {"key_list": [
|
603 |
+
["transformer.transformer_blocks.{}.ff_context.net.0.proj.lora_A.weight",
|
604 |
+
"transformer.transformer_blocks.{}.ff_context.net.0.proj.lora_B.weight"]
|
605 |
+
], "num": 19},
|
606 |
+
"double_blocks.{}.txt_mlp.2.weight": {"key_list": [
|
607 |
+
["transformer.transformer_blocks.{}.ff_context.net.2.lora_A.weight",
|
608 |
+
"transformer.transformer_blocks.{}.ff_context.net.2.lora_B.weight"]
|
609 |
+
], "num": 19},
|
610 |
+
"double_blocks.{}.img_mod.lin.weight": {"key_list": [
|
611 |
+
["transformer.transformer_blocks.{}.norm1.linear.lora_A.weight",
|
612 |
+
"transformer.transformer_blocks.{}.norm1.linear.lora_B.weight"]
|
613 |
+
], "num": 19},
|
614 |
+
"double_blocks.{}.txt_mod.lin.weight": {"key_list": [
|
615 |
+
["transformer.transformer_blocks.{}.norm1_context.linear.lora_A.weight",
|
616 |
+
"transformer.transformer_blocks.{}.norm1_context.linear.lora_B.weight"]
|
617 |
+
], "num": 19}
|
618 |
+
}
|
619 |
+
for k, v in key_map.items():
|
620 |
+
key_list = v["key_list"]
|
621 |
+
block_num = v["num"]
|
622 |
+
for block_id in range(block_num):
|
623 |
+
current_weight_list = []
|
624 |
+
for k_list in key_list:
|
625 |
+
current_weight = torch.matmul(lora_sd[k_list[0].format(block_id)].permute(1, 0),
|
626 |
+
lora_sd[k_list[1].format(block_id)].permute(1, 0)).permute(1, 0)
|
627 |
+
current_weight_list.append(current_weight)
|
628 |
+
current_weight = torch.cat(current_weight_list, dim=0)
|
629 |
+
ori_sd[k.format(block_id)] += scale*current_weight
|
630 |
+
return ori_sd
|
631 |
+
|
632 |
+
def merge_swift_lora(self, ori_sd, lora_sd, scale = 1.0):
|
633 |
+
have_lora_keys = {}
|
634 |
+
for k, v in lora_sd.items():
|
635 |
+
k = k[len("model."):] if k.startswith("model.") else k
|
636 |
+
ori_key = k.split("lora")[0] + "weight"
|
637 |
+
if ori_key not in ori_sd:
|
638 |
+
raise f"{ori_key} should in the original statedict"
|
639 |
+
if ori_key not in have_lora_keys:
|
640 |
+
have_lora_keys[ori_key] = {}
|
641 |
+
if "lora_A" in k:
|
642 |
+
have_lora_keys[ori_key]["lora_A"] = v
|
643 |
+
elif "lora_B" in k:
|
644 |
+
have_lora_keys[ori_key]["lora_B"] = v
|
645 |
+
else:
|
646 |
+
raise NotImplementedError
|
647 |
+
for key, v in have_lora_keys.items():
|
648 |
+
current_weight = torch.matmul(v["lora_A"].permute(1, 0), v["lora_B"].permute(1, 0)).permute(1, 0)
|
649 |
+
ori_sd[key] += scale * current_weight
|
650 |
+
return ori_sd
|
651 |
+
|
652 |
+
|
653 |
+
def load_pretrained_model(self, pretrained_model):
|
654 |
+
if next(self.parameters()).device.type == 'meta':
|
655 |
+
map_location = we.device_id
|
656 |
+
else:
|
657 |
+
map_location = "cpu"
|
658 |
+
if self.lora_model is not None:
|
659 |
+
map_location = we.device_id
|
660 |
+
if pretrained_model is not None:
|
661 |
+
with FS.get_from(pretrained_model, wait_finish=True) as local_model:
|
662 |
+
if local_model.endswith('safetensors'):
|
663 |
+
from safetensors.torch import load_file as load_safetensors
|
664 |
+
sd = load_safetensors(local_model, device=map_location)
|
665 |
+
else:
|
666 |
+
sd = torch.load(local_model, map_location=map_location)
|
667 |
+
if "state_dict" in sd:
|
668 |
+
sd = sd["state_dict"]
|
669 |
+
if "model" in sd:
|
670 |
+
sd = sd["model"]["model"]
|
671 |
+
|
672 |
+
if self.lora_model is not None:
|
673 |
+
with FS.get_from(self.lora_model, wait_finish=True) as local_model:
|
674 |
+
if local_model.endswith('safetensors'):
|
675 |
+
from safetensors.torch import load_file as load_safetensors
|
676 |
+
lora_sd = load_safetensors(local_model, device=map_location)
|
677 |
+
else:
|
678 |
+
lora_sd = torch.load(local_model, map_location=map_location)
|
679 |
+
sd = self.merge_diffuser_lora(sd, lora_sd)
|
680 |
+
if self.swift_lora_model is not None:
|
681 |
+
with FS.get_from(self.swift_lora_model, wait_finish=True) as local_model:
|
682 |
+
if local_model.endswith('safetensors'):
|
683 |
+
from safetensors.torch import load_file as load_safetensors
|
684 |
+
lora_sd = load_safetensors(local_model, device=map_location)
|
685 |
+
else:
|
686 |
+
lora_sd = torch.load(local_model, map_location=map_location)
|
687 |
+
sd = self.merge_swift_lora(sd, lora_sd)
|
688 |
+
|
689 |
+
adapter_ckpt = {}
|
690 |
+
if self.pretrain_adapter is not None:
|
691 |
+
with FS.get_from(self.pretrain_adapter, wait_finish=True) as local_adapter:
|
692 |
+
if local_model.endswith('safetensors'):
|
693 |
+
from safetensors.torch import load_file as load_safetensors
|
694 |
+
adapter_ckpt = load_safetensors(local_adapter, device=map_location)
|
695 |
+
else:
|
696 |
+
adapter_ckpt = torch.load(local_adapter, map_location=map_location)
|
697 |
+
sd.update(adapter_ckpt)
|
698 |
+
|
699 |
+
|
700 |
+
new_ckpt = OrderedDict()
|
701 |
+
for k, v in sd.items():
|
702 |
+
if k in ("img_in.weight"):
|
703 |
+
model_p = self.state_dict()[k]
|
704 |
+
if v.shape != model_p.shape:
|
705 |
+
model_p.zero_()
|
706 |
+
model_p[:, :64].copy_(v[:, :64])
|
707 |
+
new_ckpt[k] = torch.nn.parameter.Parameter(model_p)
|
708 |
+
else:
|
709 |
+
new_ckpt[k] = v
|
710 |
+
else:
|
711 |
+
new_ckpt[k] = v
|
712 |
+
|
713 |
+
|
714 |
+
missing, unexpected = self.load_state_dict(new_ckpt, strict=False, assign=True)
|
715 |
+
self.logger.info(
|
716 |
+
f'Restored from {pretrained_model} with {len(missing)} missing and {len(unexpected)} unexpected keys'
|
717 |
+
)
|
718 |
+
if len(missing) > 0:
|
719 |
+
self.logger.info(f'Missing Keys:\n {missing}')
|
720 |
+
if len(unexpected) > 0:
|
721 |
+
self.logger.info(f'\nUnexpected Keys:\n {unexpected}')
|
722 |
+
|
723 |
+
def forward(
|
724 |
+
self,
|
725 |
+
x: Tensor,
|
726 |
+
t: Tensor,
|
727 |
+
cond: dict = {},
|
728 |
+
guidance: Tensor | None = None,
|
729 |
+
gc_seg: int = 0
|
730 |
+
) -> Tensor:
|
731 |
+
x, x_ids, txt, txt_ids, y, h, w = self.prepare_input(x, cond["context"], cond["y"])
|
732 |
+
# running on sequences img
|
733 |
+
x = self.img_in(x)
|
734 |
+
vec = self.time_in(timestep_embedding(t, 256))
|
735 |
+
if self.guidance_embed:
|
736 |
+
if guidance is None:
|
737 |
+
raise ValueError("Didn't get guidance strength for guidance distilled model.")
|
738 |
+
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
|
739 |
+
vec = vec + self.vector_in(y)
|
740 |
+
txt = self.txt_in(txt)
|
741 |
+
ids = torch.cat((txt_ids, x_ids), dim=1)
|
742 |
+
pe = self.pe_embedder(ids)
|
743 |
+
kwargs = dict(
|
744 |
+
vec=vec,
|
745 |
+
pe=pe,
|
746 |
+
txt_length=txt.shape[1],
|
747 |
+
)
|
748 |
+
x = torch.cat((txt, x), 1)
|
749 |
+
if self.use_grad_checkpoint and gc_seg >= 0:
|
750 |
+
x = checkpoint_sequential(
|
751 |
+
functions=[partial(block, **kwargs) for block in self.double_blocks],
|
752 |
+
segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
|
753 |
+
input=x,
|
754 |
+
use_reentrant=False
|
755 |
+
)
|
756 |
+
else:
|
757 |
+
for block in self.double_blocks:
|
758 |
+
x = block(x, **kwargs)
|
759 |
+
|
760 |
+
kwargs = dict(
|
761 |
+
vec=vec,
|
762 |
+
pe=pe,
|
763 |
+
)
|
764 |
+
|
765 |
+
if self.use_grad_checkpoint and gc_seg >= 0:
|
766 |
+
x = checkpoint_sequential(
|
767 |
+
functions=[partial(block, **kwargs) for block in self.single_blocks],
|
768 |
+
segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
|
769 |
+
input=x,
|
770 |
+
use_reentrant=False
|
771 |
+
)
|
772 |
+
else:
|
773 |
+
for block in self.single_blocks:
|
774 |
+
x = block(x, **kwargs)
|
775 |
+
x = x[:, txt.shape[1] :, ...]
|
776 |
+
x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64
|
777 |
+
x = self.unpack(x, h, w)
|
778 |
+
return x
|
779 |
+
|
780 |
+
@staticmethod
|
781 |
+
def get_config_template():
|
782 |
+
return dict_to_yaml('MODEL',
|
783 |
+
__class__.__name__,
|
784 |
+
Flux.para_dict,
|
785 |
+
set_name=True)
|
786 |
+
|
787 |
+
@BACKBONES.register_class()
|
788 |
+
class FluxMR(Flux):
|
789 |
+
def prepare_input(self, x, cond):
|
790 |
+
if isinstance(cond['context'], list):
|
791 |
+
context, y = torch.cat(cond["context"], dim=0).to(x), torch.cat(cond["y"], dim=0).to(x)
|
792 |
+
else:
|
793 |
+
context, y = cond['context'].to(x), cond['y'].to(x)
|
794 |
+
batch_frames, batch_frames_ids = [], []
|
795 |
+
for ix, shape in zip(x, cond["x_shapes"]):
|
796 |
+
# unpack image from sequence
|
797 |
+
ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1])
|
798 |
+
c, h, w = ix.shape
|
799 |
+
ix = rearrange(ix, "c (h ph) (w pw) -> (h w) (c ph pw)", ph=2, pw=2)
|
800 |
+
ix_id = torch.zeros(h // 2, w // 2, 3)
|
801 |
+
ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None]
|
802 |
+
ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :]
|
803 |
+
ix_id = rearrange(ix_id, "h w c -> (h w) c")
|
804 |
+
batch_frames.append([ix])
|
805 |
+
batch_frames_ids.append([ix_id])
|
806 |
+
|
807 |
+
x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], []
|
808 |
+
for frames, frame_ids in zip(batch_frames, batch_frames_ids):
|
809 |
+
proj_frames = []
|
810 |
+
for idx, one_frame in enumerate(frames):
|
811 |
+
one_frame = self.img_in(one_frame)
|
812 |
+
proj_frames.append(one_frame)
|
813 |
+
ix = torch.cat(proj_frames, dim=0)
|
814 |
+
if_id = torch.cat(frame_ids, dim=0)
|
815 |
+
x_list.append(ix)
|
816 |
+
x_id_list.append(if_id)
|
817 |
+
mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool())
|
818 |
+
x_seq_length.append(ix.shape[0])
|
819 |
+
x = pad_sequence(tuple(x_list), batch_first=True)
|
820 |
+
x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2
|
821 |
+
mask_x = pad_sequence(tuple(mask_x_list), batch_first=True)
|
822 |
+
|
823 |
+
txt = self.txt_in(context)
|
824 |
+
txt_ids = torch.zeros(context.shape[0], context.shape[1], 3).to(x)
|
825 |
+
mask_txt = torch.ones(context.shape[0], context.shape[1]).to(x.device, non_blocking=True).bool()
|
826 |
+
|
827 |
+
return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length
|
828 |
+
|
829 |
+
def unpack(self, x: Tensor, cond: dict = None, x_seq_length: list = None) -> Tensor:
|
830 |
+
x_list = []
|
831 |
+
image_shapes = cond["x_shapes"]
|
832 |
+
for u, shape, seq_length in zip(x, image_shapes, x_seq_length):
|
833 |
+
height, width = shape
|
834 |
+
h, w = math.ceil(height / 2), math.ceil(width / 2)
|
835 |
+
u = rearrange(
|
836 |
+
u[seq_length-h*w:seq_length, ...],
|
837 |
+
"(h w) (c ph pw) -> (h ph w pw) c",
|
838 |
+
h=h,
|
839 |
+
w=w,
|
840 |
+
ph=2,
|
841 |
+
pw=2,
|
842 |
+
)
|
843 |
+
x_list.append(u)
|
844 |
+
x = pad_sequence(tuple(x_list), batch_first=True).permute(0, 2, 1)
|
845 |
+
return x
|
846 |
+
|
847 |
+
def forward(
|
848 |
+
self,
|
849 |
+
x: Tensor,
|
850 |
+
t: Tensor,
|
851 |
+
cond: dict = {},
|
852 |
+
guidance: Tensor | None = None,
|
853 |
+
gc_seg: int = 0,
|
854 |
+
**kwargs
|
855 |
+
) -> Tensor:
|
856 |
+
x, x_ids, txt, txt_ids, y, mask_x, mask_txt, seq_length_list = self.prepare_input(x, cond)
|
857 |
+
# running on sequences img
|
858 |
+
vec = self.time_in(timestep_embedding(t, 256))
|
859 |
+
if self.guidance_embed:
|
860 |
+
if guidance is None:
|
861 |
+
raise ValueError("Didn't get guidance strength for guidance distilled model.")
|
862 |
+
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
|
863 |
+
vec = vec + self.vector_in(y)
|
864 |
+
ids = torch.cat((txt_ids, x_ids), dim=1)
|
865 |
+
pe = self.pe_embedder(ids)
|
866 |
+
|
867 |
+
mask_aside = torch.cat((mask_txt, mask_x), dim=1)
|
868 |
+
mask = mask_aside[:, None, :] * mask_aside[:, :, None]
|
869 |
+
|
870 |
+
kwargs = dict(
|
871 |
+
vec=vec,
|
872 |
+
pe=pe,
|
873 |
+
mask=mask,
|
874 |
+
txt_length = txt.shape[1],
|
875 |
+
)
|
876 |
+
x = torch.cat((txt, x), 1)
|
877 |
+
if self.use_grad_checkpoint and gc_seg >= 0:
|
878 |
+
x = checkpoint_sequential(
|
879 |
+
functions=[partial(block, **kwargs) for block in self.double_blocks],
|
880 |
+
segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
|
881 |
+
input=x,
|
882 |
+
use_reentrant=False
|
883 |
+
)
|
884 |
+
else:
|
885 |
+
for block in self.double_blocks:
|
886 |
+
x = block(x, **kwargs)
|
887 |
+
|
888 |
+
kwargs = dict(
|
889 |
+
vec=vec,
|
890 |
+
pe=pe,
|
891 |
+
mask=mask,
|
892 |
+
)
|
893 |
+
|
894 |
+
if self.use_grad_checkpoint and gc_seg >= 0:
|
895 |
+
x = checkpoint_sequential(
|
896 |
+
functions=[partial(block, **kwargs) for block in self.single_blocks],
|
897 |
+
segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
|
898 |
+
input=x,
|
899 |
+
use_reentrant=False
|
900 |
+
)
|
901 |
+
else:
|
902 |
+
for block in self.single_blocks:
|
903 |
+
x = block(x, **kwargs)
|
904 |
+
x = x[:, txt.shape[1]:, ...]
|
905 |
+
x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64
|
906 |
+
x = self.unpack(x, cond, seq_length_list)
|
907 |
+
return x
|
908 |
+
|
909 |
+
@staticmethod
|
910 |
+
def get_config_template():
|
911 |
+
return dict_to_yaml('MODEL',
|
912 |
+
__class__.__name__,
|
913 |
+
FluxEdit.para_dict,
|
914 |
+
set_name=True)
|
915 |
+
@BACKBONES.register_class()
|
916 |
+
class FluxEdit(FluxMR):
|
917 |
+
def prepare_input(self, x, cond, *args, **kwargs):
|
918 |
+
context, y = cond["context"], cond["y"]
|
919 |
+
batch_frames, batch_frames_ids, batch_shift = [], [], []
|
920 |
+
|
921 |
+
for ix, shape, is_align in zip(x, cond["x_shapes"], cond['align']):
|
922 |
+
# unpack image from sequence
|
923 |
+
ix = ix[:, :shape[0] * shape[1]].view(-1, shape[0], shape[1])
|
924 |
+
c, h, w = ix.shape
|
925 |
+
ix = rearrange(ix, "c (h ph) (w pw) -> (h w) (c ph pw)", ph=2, pw=2)
|
926 |
+
ix_id = torch.zeros(h // 2, w // 2, 3)
|
927 |
+
ix_id[..., 1] = ix_id[..., 1] + torch.arange(h // 2)[:, None]
|
928 |
+
ix_id[..., 2] = ix_id[..., 2] + torch.arange(w // 2)[None, :]
|
929 |
+
batch_shift.append(h // 2) #if is_align < 1 else batch_shift.append(0)
|
930 |
+
ix_id = rearrange(ix_id, "h w c -> (h w) c")
|
931 |
+
batch_frames.append([ix])
|
932 |
+
batch_frames_ids.append([ix_id])
|
933 |
+
if 'edit_x' in cond:
|
934 |
+
for i, edit in enumerate(cond['edit_x']):
|
935 |
+
if edit is None:
|
936 |
+
continue
|
937 |
+
for ie in edit:
|
938 |
+
ie = ie.squeeze(0)
|
939 |
+
c, h, w = ie.shape
|
940 |
+
ie = rearrange(ie, "c (h ph) (w pw) -> (h w) (c ph pw)", ph=2, pw=2)
|
941 |
+
ie_id = torch.zeros(h // 2, w // 2, 3)
|
942 |
+
ie_id[..., 1] = ie_id[..., 1] + torch.arange(batch_shift[i], h // 2 + batch_shift[i])[:, None]
|
943 |
+
ie_id[..., 2] = ie_id[..., 2] + torch.arange(w // 2)[None, :]
|
944 |
+
ie_id = rearrange(ie_id, "h w c -> (h w) c")
|
945 |
+
batch_frames[i].append(ie)
|
946 |
+
batch_frames_ids[i].append(ie_id)
|
947 |
+
|
948 |
+
x_list, x_id_list, mask_x_list, x_seq_length = [], [], [], []
|
949 |
+
for frames, frame_ids in zip(batch_frames, batch_frames_ids):
|
950 |
+
proj_frames = []
|
951 |
+
for idx, one_frame in enumerate(frames):
|
952 |
+
one_frame = self.img_in(one_frame)
|
953 |
+
proj_frames.append(one_frame)
|
954 |
+
ix = torch.cat(proj_frames, dim=0)
|
955 |
+
if_id = torch.cat(frame_ids, dim=0)
|
956 |
+
x_list.append(ix)
|
957 |
+
x_id_list.append(if_id)
|
958 |
+
mask_x_list.append(torch.ones(ix.shape[0]).to(ix.device, non_blocking=True).bool())
|
959 |
+
x_seq_length.append(ix.shape[0])
|
960 |
+
x = pad_sequence(tuple(x_list), batch_first=True)
|
961 |
+
x_ids = pad_sequence(tuple(x_id_list), batch_first=True).to(x) # [b,pad_seq,2] pad (0.,0.) at dim2
|
962 |
+
mask_x = pad_sequence(tuple(mask_x_list), batch_first=True)
|
963 |
+
|
964 |
+
txt_list, mask_txt_list, y_list = [], [], []
|
965 |
+
for sample_id, (ctx, yy) in enumerate(zip(context, y)):
|
966 |
+
ctx_batch = []
|
967 |
+
for frame_id, one_ctx in enumerate(ctx):
|
968 |
+
one_ctx = self.txt_in(one_ctx.to(x))
|
969 |
+
ctx_batch.append(one_ctx)
|
970 |
+
txt_list.append(torch.cat(ctx_batch, dim=0))
|
971 |
+
mask_txt_list.append(torch.ones(txt_list[-1].shape[0]).to(ctx.device, non_blocking=True).bool())
|
972 |
+
y_list.append(yy.mean(dim = 0, keepdim=True))
|
973 |
+
txt = pad_sequence(tuple(txt_list), batch_first=True)
|
974 |
+
txt_ids = torch.zeros(txt.shape[0], txt.shape[1], 3).to(x)
|
975 |
+
mask_txt = pad_sequence(tuple(mask_txt_list), batch_first=True)
|
976 |
+
y = torch.cat(y_list, dim=0)
|
977 |
+
return x, x_ids, txt, txt_ids, y, mask_x, mask_txt, x_seq_length
|
978 |
+
|
979 |
+
def unpack(self, x: Tensor, cond: dict = None, x_seq_length: list = None) -> Tensor:
|
980 |
+
x_list = []
|
981 |
+
image_shapes = cond["x_shapes"]
|
982 |
+
for u, shape, seq_length in zip(x, image_shapes, x_seq_length):
|
983 |
+
height, width = shape
|
984 |
+
h, w = math.ceil(height / 2), math.ceil(width / 2)
|
985 |
+
u = rearrange(
|
986 |
+
u[:h*w, ...],
|
987 |
+
"(h w) (c ph pw) -> (h ph w pw) c",
|
988 |
+
h=h,
|
989 |
+
w=w,
|
990 |
+
ph=2,
|
991 |
+
pw=2,
|
992 |
+
)
|
993 |
+
x_list.append(u)
|
994 |
+
x = pad_sequence(tuple(x_list), batch_first=True).permute(0, 2, 1)
|
995 |
+
return x
|
996 |
+
|
997 |
+
def forward(
|
998 |
+
self,
|
999 |
+
x: Tensor,
|
1000 |
+
t: Tensor,
|
1001 |
+
cond: dict = {},
|
1002 |
+
guidance: Tensor | None = None,
|
1003 |
+
gc_seg: int = 0,
|
1004 |
+
text_position_embeddings = None
|
1005 |
+
) -> Tensor:
|
1006 |
+
x, x_ids, txt, txt_ids, y, mask_x, mask_txt, seq_length_list = self.prepare_input(x, cond, text_position_embeddings)
|
1007 |
+
# running on sequences img
|
1008 |
+
vec = self.time_in(timestep_embedding(t, 256))
|
1009 |
+
if self.guidance_embed:
|
1010 |
+
if guidance is None:
|
1011 |
+
raise ValueError("Didn't get guidance strength for guidance distilled model.")
|
1012 |
+
vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
|
1013 |
+
vec = vec + self.vector_in(y)
|
1014 |
+
ids = torch.cat((txt_ids, x_ids), dim=1)
|
1015 |
+
pe = self.pe_embedder(ids)
|
1016 |
+
|
1017 |
+
mask_aside = torch.cat((mask_txt, mask_x), dim=1)
|
1018 |
+
mask = mask_aside[:, None, :] * mask_aside[:, :, None]
|
1019 |
+
|
1020 |
+
kwargs = dict(
|
1021 |
+
vec=vec,
|
1022 |
+
pe=pe,
|
1023 |
+
mask=mask,
|
1024 |
+
txt_length = txt.shape[1],
|
1025 |
+
)
|
1026 |
+
x = torch.cat((txt, x), 1)
|
1027 |
+
|
1028 |
+
if self.use_grad_checkpoint and gc_seg >= 0:
|
1029 |
+
x = checkpoint_sequential(
|
1030 |
+
functions=[partial(block, **kwargs) for block in self.double_blocks],
|
1031 |
+
segments=gc_seg if gc_seg > 0 else len(self.double_blocks),
|
1032 |
+
input=x,
|
1033 |
+
use_reentrant=False
|
1034 |
+
)
|
1035 |
+
else:
|
1036 |
+
for block in self.double_blocks:
|
1037 |
+
x = block(x, **kwargs)
|
1038 |
+
|
1039 |
+
kwargs = dict(
|
1040 |
+
vec=vec,
|
1041 |
+
pe=pe,
|
1042 |
+
mask=mask,
|
1043 |
+
)
|
1044 |
+
|
1045 |
+
if self.use_grad_checkpoint and gc_seg >= 0:
|
1046 |
+
x = checkpoint_sequential(
|
1047 |
+
functions=[partial(block, **kwargs) for block in self.single_blocks],
|
1048 |
+
segments=gc_seg if gc_seg > 0 else len(self.single_blocks),
|
1049 |
+
input=x,
|
1050 |
+
use_reentrant=False
|
1051 |
+
)
|
1052 |
+
else:
|
1053 |
+
for block in self.single_blocks:
|
1054 |
+
x = block(x, **kwargs)
|
1055 |
+
x = x[:, txt.shape[1]:, ...]
|
1056 |
+
x = self.final_layer(x, vec) # (N, T, patch_size ** 2 * out_channels) 6 64 64
|
1057 |
+
x = self.unpack(x, cond, seq_length_list)
|
1058 |
+
return x
|
1059 |
+
@staticmethod
|
1060 |
+
def get_config_template():
|
1061 |
+
return dict_to_yaml('MODEL',
|
1062 |
+
__class__.__name__,
|
1063 |
+
FluxEdit.para_dict,
|
1064 |
+
set_name=True)
|
model/layers.py
ADDED
@@ -0,0 +1,356 @@
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from __future__ import annotations
|
2 |
+
|
3 |
+
import math
|
4 |
+
from dataclasses import dataclass
|
5 |
+
from torch import Tensor, nn
|
6 |
+
import torch
|
7 |
+
from einops import rearrange, repeat
|
8 |
+
from torch import Tensor
|
9 |
+
from torch.nn.utils.rnn import pad_sequence
|
10 |
+
|
11 |
+
try:
|
12 |
+
from flash_attn import (
|
13 |
+
flash_attn_varlen_func
|
14 |
+
)
|
15 |
+
FLASHATTN_IS_AVAILABLE = True
|
16 |
+
except ImportError:
|
17 |
+
FLASHATTN_IS_AVAILABLE = False
|
18 |
+
flash_attn_varlen_func = None
|
19 |
+
|
20 |
+
def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask: Tensor | None = None, backend = 'pytorch') -> Tensor:
|
21 |
+
q, k = apply_rope(q, k, pe)
|
22 |
+
if backend == 'pytorch':
|
23 |
+
if mask is not None and mask.dtype == torch.bool:
|
24 |
+
mask = torch.zeros_like(mask).to(q).masked_fill_(mask.logical_not(), -1e20)
|
25 |
+
x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask)
|
26 |
+
# x = torch.nan_to_num(x, nan=0.0, posinf=1e10, neginf=-1e10)
|
27 |
+
x = rearrange(x, "B H L D -> B L (H D)")
|
28 |
+
elif backend == 'flash_attn':
|
29 |
+
# q: (B, H, L, D)
|
30 |
+
# k: (B, H, S, D) now L = S
|
31 |
+
# v: (B, H, S, D)
|
32 |
+
b, h, lq, d = q.shape
|
33 |
+
_, _, lk, _ = k.shape
|
34 |
+
q = rearrange(q, "B H L D -> B L H D")
|
35 |
+
k = rearrange(k, "B H S D -> B S H D")
|
36 |
+
v = rearrange(v, "B H S D -> B S H D")
|
37 |
+
if mask is None:
|
38 |
+
q_lens = torch.tensor([lq] * b, dtype=torch.int32).to(q.device, non_blocking=True)
|
39 |
+
k_lens = torch.tensor([lk] * b, dtype=torch.int32).to(k.device, non_blocking=True)
|
40 |
+
else:
|
41 |
+
q_lens = torch.sum(mask[:, 0, :, 0], dim=1).int()
|
42 |
+
k_lens = torch.sum(mask[:, 0, 0, :], dim=1).int()
|
43 |
+
q = torch.cat([q_v[:q_l] for q_v, q_l in zip(q, q_lens)])
|
44 |
+
k = torch.cat([k_v[:k_l] for k_v, k_l in zip(k, k_lens)])
|
45 |
+
v = torch.cat([v_v[:v_l] for v_v, v_l in zip(v, k_lens)])
|
46 |
+
cu_seqlens_q = torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(0, dtype=torch.int32)
|
47 |
+
cu_seqlens_k = torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(0, dtype=torch.int32)
|
48 |
+
max_seqlen_q = q_lens.max()
|
49 |
+
max_seqlen_k = k_lens.max()
|
50 |
+
|
51 |
+
x = flash_attn_varlen_func(
|
52 |
+
q,
|
53 |
+
k,
|
54 |
+
v,
|
55 |
+
cu_seqlens_q=cu_seqlens_q,
|
56 |
+
cu_seqlens_k=cu_seqlens_k,
|
57 |
+
max_seqlen_q=max_seqlen_q,
|
58 |
+
max_seqlen_k=max_seqlen_k
|
59 |
+
)
|
60 |
+
x_list = [x[cu_seqlens_q[i]:cu_seqlens_q[i+1]] for i in range(b)]
|
61 |
+
x = pad_sequence(tuple(x_list), batch_first=True)
|
62 |
+
x = rearrange(x, "B L H D -> B L (H D)")
|
63 |
+
else:
|
64 |
+
raise NotImplementedError
|
65 |
+
return x
|
66 |
+
|
67 |
+
|
68 |
+
def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
|
69 |
+
assert dim % 2 == 0
|
70 |
+
scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
|
71 |
+
omega = 1.0 / (theta**scale)
|
72 |
+
out = torch.einsum("...n,d->...nd", pos, omega)
|
73 |
+
out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
|
74 |
+
out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
|
75 |
+
return out.float()
|
76 |
+
|
77 |
+
|
78 |
+
def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
|
79 |
+
xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
|
80 |
+
xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
|
81 |
+
xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
|
82 |
+
xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
|
83 |
+
return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
|
84 |
+
|
85 |
+
class EmbedND(nn.Module):
|
86 |
+
def __init__(self, dim: int, theta: int, axes_dim: list[int]):
|
87 |
+
super().__init__()
|
88 |
+
self.dim = dim
|
89 |
+
self.theta = theta
|
90 |
+
self.axes_dim = axes_dim
|
91 |
+
|
92 |
+
def forward(self, ids: Tensor) -> Tensor:
|
93 |
+
n_axes = ids.shape[-1]
|
94 |
+
emb = torch.cat(
|
95 |
+
[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
|
96 |
+
dim=-3,
|
97 |
+
)
|
98 |
+
|
99 |
+
return emb.unsqueeze(1)
|
100 |
+
|
101 |
+
|
102 |
+
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
|
103 |
+
"""
|
104 |
+
Create sinusoidal timestep embeddings.
|
105 |
+
:param t: a 1-D Tensor of N indices, one per batch element.
|
106 |
+
These may be fractional.
|
107 |
+
:param dim: the dimension of the output.
|
108 |
+
:param max_period: controls the minimum frequency of the embeddings.
|
109 |
+
:return: an (N, D) Tensor of positional embeddings.
|
110 |
+
"""
|
111 |
+
t = time_factor * t
|
112 |
+
half = dim // 2
|
113 |
+
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
|
114 |
+
t.device
|
115 |
+
)
|
116 |
+
|
117 |
+
args = t[:, None].float() * freqs[None]
|
118 |
+
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
|
119 |
+
if dim % 2:
|
120 |
+
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
|
121 |
+
if torch.is_floating_point(t):
|
122 |
+
embedding = embedding.to(t)
|
123 |
+
return embedding
|
124 |
+
|
125 |
+
|
126 |
+
class MLPEmbedder(nn.Module):
|
127 |
+
def __init__(self, in_dim: int, hidden_dim: int):
|
128 |
+
super().__init__()
|
129 |
+
self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
|
130 |
+
self.silu = nn.SiLU()
|
131 |
+
self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
|
132 |
+
|
133 |
+
def forward(self, x: Tensor) -> Tensor:
|
134 |
+
return self.out_layer(self.silu(self.in_layer(x)))
|
135 |
+
|
136 |
+
|
137 |
+
class RMSNorm(torch.nn.Module):
|
138 |
+
def __init__(self, dim: int):
|
139 |
+
super().__init__()
|
140 |
+
self.scale = nn.Parameter(torch.ones(dim))
|
141 |
+
|
142 |
+
def forward(self, x: Tensor):
|
143 |
+
x_dtype = x.dtype
|
144 |
+
x = x.float()
|
145 |
+
rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
|
146 |
+
return (x * rrms).to(dtype=x_dtype) * self.scale
|
147 |
+
|
148 |
+
|
149 |
+
class QKNorm(torch.nn.Module):
|
150 |
+
def __init__(self, dim: int):
|
151 |
+
super().__init__()
|
152 |
+
self.query_norm = RMSNorm(dim)
|
153 |
+
self.key_norm = RMSNorm(dim)
|
154 |
+
|
155 |
+
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
|
156 |
+
q = self.query_norm(q)
|
157 |
+
k = self.key_norm(k)
|
158 |
+
return q.to(v), k.to(v)
|
159 |
+
|
160 |
+
|
161 |
+
class SelfAttention(nn.Module):
|
162 |
+
def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
|
163 |
+
super().__init__()
|
164 |
+
self.num_heads = num_heads
|
165 |
+
head_dim = dim // num_heads
|
166 |
+
|
167 |
+
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
168 |
+
self.norm = QKNorm(head_dim)
|
169 |
+
self.proj = nn.Linear(dim, dim)
|
170 |
+
|
171 |
+
def forward(self, x: Tensor, pe: Tensor, mask: Tensor | None = None) -> Tensor:
|
172 |
+
qkv = self.qkv(x)
|
173 |
+
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
174 |
+
q, k = self.norm(q, k, v)
|
175 |
+
x = attention(q, k, v, pe=pe, mask=mask)
|
176 |
+
x = self.proj(x)
|
177 |
+
return x
|
178 |
+
|
179 |
+
class CrossAttention(nn.Module):
|
180 |
+
def __init__(self, dim: int, context_dim: int, num_heads: int = 8, qkv_bias: bool = False):
|
181 |
+
super().__init__()
|
182 |
+
self.num_heads = num_heads
|
183 |
+
head_dim = dim // num_heads
|
184 |
+
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
185 |
+
self.kv = nn.Linear(dim, context_dim * 2, bias=qkv_bias)
|
186 |
+
self.norm = QKNorm(head_dim)
|
187 |
+
self.proj = nn.Linear(dim, dim)
|
188 |
+
|
189 |
+
def forward(self, x: Tensor, context: Tensor, pe: Tensor, mask: Tensor | None = None) -> Tensor:
|
190 |
+
qkv = self.qkv(x)
|
191 |
+
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
192 |
+
q, k = self.norm(q, k, v)
|
193 |
+
x = attention(q, k, v, pe=pe, mask=mask)
|
194 |
+
x = self.proj(x)
|
195 |
+
return x
|
196 |
+
|
197 |
+
|
198 |
+
@dataclass
|
199 |
+
class ModulationOut:
|
200 |
+
shift: Tensor
|
201 |
+
scale: Tensor
|
202 |
+
gate: Tensor
|
203 |
+
|
204 |
+
|
205 |
+
class Modulation(nn.Module):
|
206 |
+
def __init__(self, dim: int, double: bool):
|
207 |
+
super().__init__()
|
208 |
+
self.is_double = double
|
209 |
+
self.multiplier = 6 if double else 3
|
210 |
+
self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
|
211 |
+
|
212 |
+
def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
|
213 |
+
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
|
214 |
+
|
215 |
+
return (
|
216 |
+
ModulationOut(*out[:3]),
|
217 |
+
ModulationOut(*out[3:]) if self.is_double else None,
|
218 |
+
)
|
219 |
+
|
220 |
+
|
221 |
+
class DoubleStreamBlock(nn.Module):
|
222 |
+
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, backend = 'pytorch'):
|
223 |
+
super().__init__()
|
224 |
+
|
225 |
+
mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
226 |
+
self.num_heads = num_heads
|
227 |
+
self.hidden_size = hidden_size
|
228 |
+
self.img_mod = Modulation(hidden_size, double=True)
|
229 |
+
self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
230 |
+
self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
|
231 |
+
|
232 |
+
self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
233 |
+
self.img_mlp = nn.Sequential(
|
234 |
+
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
235 |
+
nn.GELU(approximate="tanh"),
|
236 |
+
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
237 |
+
)
|
238 |
+
|
239 |
+
self.backend = backend
|
240 |
+
|
241 |
+
self.txt_mod = Modulation(hidden_size, double=True)
|
242 |
+
self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
243 |
+
self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
|
244 |
+
|
245 |
+
self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
246 |
+
self.txt_mlp = nn.Sequential(
|
247 |
+
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
248 |
+
nn.GELU(approximate="tanh"),
|
249 |
+
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
250 |
+
)
|
251 |
+
|
252 |
+
|
253 |
+
|
254 |
+
|
255 |
+
def forward(self, x: Tensor, vec: Tensor, pe: Tensor, mask: Tensor = None, txt_length = None):
|
256 |
+
img_mod1, img_mod2 = self.img_mod(vec)
|
257 |
+
txt_mod1, txt_mod2 = self.txt_mod(vec)
|
258 |
+
|
259 |
+
txt, img = x[:, :txt_length], x[:, txt_length:]
|
260 |
+
|
261 |
+
# prepare image for attention
|
262 |
+
img_modulated = self.img_norm1(img)
|
263 |
+
img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
|
264 |
+
img_qkv = self.img_attn.qkv(img_modulated)
|
265 |
+
img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
266 |
+
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
|
267 |
+
# prepare txt for attention
|
268 |
+
txt_modulated = self.txt_norm1(txt)
|
269 |
+
txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
|
270 |
+
txt_qkv = self.txt_attn.qkv(txt_modulated)
|
271 |
+
txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
272 |
+
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
|
273 |
+
|
274 |
+
# run actual attention
|
275 |
+
q = torch.cat((txt_q, img_q), dim=2)
|
276 |
+
k = torch.cat((txt_k, img_k), dim=2)
|
277 |
+
v = torch.cat((txt_v, img_v), dim=2)
|
278 |
+
if mask is not None:
|
279 |
+
mask = repeat(mask, 'B L S-> B H L S', H=self.num_heads)
|
280 |
+
attn = attention(q, k, v, pe=pe, mask = mask, backend = self.backend)
|
281 |
+
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
|
282 |
+
|
283 |
+
# calculate the img bloks
|
284 |
+
img = img + img_mod1.gate * self.img_attn.proj(img_attn)
|
285 |
+
img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
|
286 |
+
|
287 |
+
# calculate the txt bloks
|
288 |
+
txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
|
289 |
+
txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
|
290 |
+
x = torch.cat((txt, img), 1)
|
291 |
+
return x
|
292 |
+
|
293 |
+
|
294 |
+
class SingleStreamBlock(nn.Module):
|
295 |
+
"""
|
296 |
+
A DiT block with parallel linear layers as described in
|
297 |
+
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
|
298 |
+
"""
|
299 |
+
|
300 |
+
def __init__(
|
301 |
+
self,
|
302 |
+
hidden_size: int,
|
303 |
+
num_heads: int,
|
304 |
+
mlp_ratio: float = 4.0,
|
305 |
+
qk_scale: float | None = None,
|
306 |
+
backend='pytorch'
|
307 |
+
):
|
308 |
+
super().__init__()
|
309 |
+
self.hidden_dim = hidden_size
|
310 |
+
self.num_heads = num_heads
|
311 |
+
head_dim = hidden_size // num_heads
|
312 |
+
self.scale = qk_scale or head_dim**-0.5
|
313 |
+
|
314 |
+
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
315 |
+
# qkv and mlp_in
|
316 |
+
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
317 |
+
# proj and mlp_out
|
318 |
+
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
319 |
+
|
320 |
+
self.norm = QKNorm(head_dim)
|
321 |
+
|
322 |
+
self.hidden_size = hidden_size
|
323 |
+
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
324 |
+
|
325 |
+
self.mlp_act = nn.GELU(approximate="tanh")
|
326 |
+
self.modulation = Modulation(hidden_size, double=False)
|
327 |
+
self.backend = backend
|
328 |
+
|
329 |
+
def forward(self, x: Tensor, vec: Tensor, pe: Tensor, mask: Tensor = None) -> Tensor:
|
330 |
+
mod, _ = self.modulation(vec)
|
331 |
+
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
|
332 |
+
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
|
333 |
+
|
334 |
+
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
|
335 |
+
q, k = self.norm(q, k, v)
|
336 |
+
if mask is not None:
|
337 |
+
mask = repeat(mask, 'B L S-> B H L S', H=self.num_heads)
|
338 |
+
# compute attention
|
339 |
+
attn = attention(q, k, v, pe=pe, mask = mask, backend=self.backend)
|
340 |
+
# compute activation in mlp stream, cat again and run second linear layer
|
341 |
+
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
|
342 |
+
return x + mod.gate * output
|
343 |
+
|
344 |
+
|
345 |
+
class LastLayer(nn.Module):
|
346 |
+
def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
|
347 |
+
super().__init__()
|
348 |
+
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
349 |
+
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
|
350 |
+
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
|
351 |
+
|
352 |
+
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
|
353 |
+
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
|
354 |
+
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
355 |
+
x = self.linear(x)
|
356 |
+
return x
|
utils.py
ADDED
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#copyright (c) Alibaba, Inc. and its affiliates.
|
2 |
+
import torch
|
3 |
+
import torchvision.transforms as T
|
4 |
+
from PIL import Image
|
5 |
+
from torchvision.transforms.functional import InterpolationMode
|
6 |
+
|
7 |
+
IMAGENET_MEAN = (0.485, 0.456, 0.406)
|
8 |
+
IMAGENET_STD = (0.229, 0.224, 0.225)
|
9 |
+
|
10 |
+
|
11 |
+
def build_transform(input_size):
|
12 |
+
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
|
13 |
+
transform = T.Compose([
|
14 |
+
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
|
15 |
+
T.Resize((input_size, input_size),
|
16 |
+
interpolation=InterpolationMode.BICUBIC),
|
17 |
+
T.ToTensor(),
|
18 |
+
T.Normalize(mean=MEAN, std=STD)
|
19 |
+
])
|
20 |
+
return transform
|
21 |
+
|
22 |
+
|
23 |
+
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height,
|
24 |
+
image_size):
|
25 |
+
best_ratio_diff = float('inf')
|
26 |
+
best_ratio = (1, 1)
|
27 |
+
area = width * height
|
28 |
+
for ratio in target_ratios:
|
29 |
+
target_aspect_ratio = ratio[0] / ratio[1]
|
30 |
+
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
|
31 |
+
if ratio_diff < best_ratio_diff:
|
32 |
+
best_ratio_diff = ratio_diff
|
33 |
+
best_ratio = ratio
|
34 |
+
elif ratio_diff == best_ratio_diff:
|
35 |
+
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
|
36 |
+
best_ratio = ratio
|
37 |
+
return best_ratio
|
38 |
+
|
39 |
+
|
40 |
+
def dynamic_preprocess(image,
|
41 |
+
min_num=1,
|
42 |
+
max_num=12,
|
43 |
+
image_size=448,
|
44 |
+
use_thumbnail=False):
|
45 |
+
orig_width, orig_height = image.size
|
46 |
+
aspect_ratio = orig_width / orig_height
|
47 |
+
|
48 |
+
# calculate the existing image aspect ratio
|
49 |
+
target_ratios = set((i, j) for n in range(min_num, max_num + 1)
|
50 |
+
for i in range(1, n + 1) for j in range(1, n + 1)
|
51 |
+
if i * j <= max_num and i * j >= min_num)
|
52 |
+
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
|
53 |
+
|
54 |
+
# find the closest aspect ratio to the target
|
55 |
+
target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio,
|
56 |
+
target_ratios, orig_width,
|
57 |
+
orig_height, image_size)
|
58 |
+
|
59 |
+
# calculate the target width and height
|
60 |
+
target_width = image_size * target_aspect_ratio[0]
|
61 |
+
target_height = image_size * target_aspect_ratio[1]
|
62 |
+
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
|
63 |
+
|
64 |
+
# resize the image
|
65 |
+
resized_img = image.resize((target_width, target_height))
|
66 |
+
processed_images = []
|
67 |
+
for i in range(blocks):
|
68 |
+
box = ((i % (target_width // image_size)) * image_size,
|
69 |
+
(i // (target_width // image_size)) * image_size,
|
70 |
+
((i % (target_width // image_size)) + 1) * image_size,
|
71 |
+
((i // (target_width // image_size)) + 1) * image_size)
|
72 |
+
# split the image
|
73 |
+
split_img = resized_img.crop(box)
|
74 |
+
processed_images.append(split_img)
|
75 |
+
assert len(processed_images) == blocks
|
76 |
+
if use_thumbnail and len(processed_images) != 1:
|
77 |
+
thumbnail_img = image.resize((image_size, image_size))
|
78 |
+
processed_images.append(thumbnail_img)
|
79 |
+
return processed_images
|
80 |
+
|
81 |
+
|
82 |
+
def load_image(image_file, input_size=448, max_num=12):
|
83 |
+
if isinstance(image_file, str):
|
84 |
+
image = Image.open(image_file).convert('RGB')
|
85 |
+
else:
|
86 |
+
image = image_file
|
87 |
+
transform = build_transform(input_size=input_size)
|
88 |
+
images = dynamic_preprocess(image,
|
89 |
+
image_size=input_size,
|
90 |
+
use_thumbnail=True,
|
91 |
+
max_num=max_num)
|
92 |
+
pixel_values = [transform(image) for image in images]
|
93 |
+
pixel_values = torch.stack(pixel_values)
|
94 |
+
return pixel_values
|
95 |
+
|