File size: 22,843 Bytes
7dd7d9c
ec7fc1c
 
7dd7d9c
ec7fc1c
5b3c0e4
 
0ba2339
ec7fc1c
 
 
7dd7d9c
ec7fc1c
 
 
 
 
0ba2339
3c49f9a
7dd7d9c
ec7fc1c
 
857a5c4
 
ec7fc1c
 
857a5c4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c49f9a
 
2192aaf
ec7fc1c
0ba2339
 
6aae2cc
0ba2339
 
 
7dd7d9c
0ba2339
7dd7d9c
 
 
 
 
0ba2339
 
 
ec7fc1c
 
 
857a5c4
ec7fc1c
0ba2339
 
857a5c4
 
 
 
0ba2339
ec7fc1c
 
0ba2339
ec7fc1c
 
 
 
0ba2339
857a5c4
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0ba2339
 
ec7fc1c
 
 
0ba2339
 
ec7fc1c
 
 
 
0ba2339
7dd7d9c
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c49f9a
 
 
 
 
 
 
 
 
0ba2339
 
 
7dd7d9c
ec7fc1c
0ba2339
 
 
 
 
7dd7d9c
ec7fc1c
 
0ba2339
 
 
 
7dd7d9c
ec7fc1c
 
0ba2339
 
 
 
7dd7d9c
ec7fc1c
 
0ba2339
 
 
 
7dd7d9c
ec7fc1c
0ba2339
 
 
 
 
 
 
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0d28f6f
0ba2339
 
 
 
 
 
7dd7d9c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ec7fc1c
 
 
7dd7d9c
 
 
ec7fc1c
 
 
0ba2339
7dd7d9c
0ba2339
7dd7d9c
 
 
 
 
 
 
 
 
 
ec7fc1c
 
 
 
7dd7d9c
 
ec7fc1c
 
 
7dd7d9c
 
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0ba2339
 
7dd7d9c
0ba2339
 
7dd7d9c
 
ec7fc1c
0ba2339
 
7dd7d9c
0ba2339
 
7dd7d9c
0ba2339
ec7fc1c
 
 
7dd7d9c
ec7fc1c
 
 
 
7dd7d9c
 
 
 
ec7fc1c
7dd7d9c
 
ec7fc1c
 
0ba2339
7dd7d9c
0ba2339
7dd7d9c
0ba2339
ec7fc1c
 
 
7dd7d9c
0ba2339
7dd7d9c
 
0ba2339
7dd7d9c
0ba2339
 
7dd7d9c
0ba2339
 
 
 
 
7dd7d9c
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0ba2339
7dd7d9c
0ba2339
 
7dd7d9c
0ba2339
 
 
 
 
ec7fc1c
0ba2339
ec7fc1c
0ba2339
 
 
 
7dd7d9c
0ba2339
3c49f9a
7dd7d9c
 
ec7fc1c
3c49f9a
 
 
 
 
 
 
 
ec7fc1c
 
 
 
 
 
3c49f9a
 
 
 
 
 
 
52ae519
ec7fc1c
 
 
 
3c49f9a
 
 
 
 
 
 
 
 
ec7fc1c
 
 
 
3c49f9a
 
7dd7d9c
3c49f9a
7dd7d9c
3c49f9a
 
 
 
 
 
 
ec7fc1c
 
 
 
 
 
 
 
 
 
7dd7d9c
3c49f9a
7dd7d9c
 
ec7fc1c
7dd7d9c
 
 
 
0ba2339
ec7fc1c
 
 
 
 
 
 
 
 
7dd7d9c
0ba2339
 
ec7fc1c
0ba2339
 
 
 
 
 
 
 
 
 
 
 
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c49f9a
 
7dd7d9c
3c49f9a
ec7fc1c
3c49f9a
7dd7d9c
3c49f9a
ec7fc1c
3c49f9a
 
ec7fc1c
3c49f9a
 
 
 
ec7fc1c
3c49f9a
ec7fc1c
3c49f9a
 
 
 
 
 
 
 
ec7fc1c
 
 
 
 
 
 
 
 
 
 
 
 
3c49f9a
0ba2339
3c49f9a
ec7fc1c
 
 
 
 
3c49f9a
 
 
7dd7d9c
3c49f9a
 
 
 
 
 
 
 
7dd7d9c
 
 
 
 
 
 
 
 
ec7fc1c
 
 
 
7dd7d9c
 
ec7fc1c
 
 
7dd7d9c
ec7fc1c
 
 
 
 
 
 
 
3c49f9a
7dd7d9c
0ba2339
 
ec7fc1c
7dd7d9c
ec7fc1c
7dd7d9c
0ba2339
 
7dd7d9c
0d28f6f
9acec60
7dd7d9c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
import cv2
import torch
import random
import numpy as np

import spaces

import PIL
from PIL import Image

import diffusers
from diffusers.utils import load_image
from diffusers.models import ControlNetModel
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel

from huggingface_hub import hf_hub_download

from insightface.app import FaceAnalysis

from style_template import styles
from pipeline_stable_diffusion_xl_instantid_full import StableDiffusionXLInstantIDPipeline, draw_kps

from controlnet_aux import OpenposeDetector
from transformers import DPTImageProcessor, DPTForDepthEstimation
import gradio as gr

def get_depth_map(image):
    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
    with torch.no_grad(), torch.autocast("cuda"):
        depth_map = depth_estimator(image).predicted_depth

    depth_map = torch.nn.functional.interpolate(
        depth_map.unsqueeze(1),
        size=(1024, 1024),
        mode="bicubic",
        align_corners=False,
    )
    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
    image = torch.cat([depth_map] * 3, dim=1)

    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
    return image

def get_canny_image(image, t1=100, t2=200):
    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    edges = cv2.Canny(image, t1, t2)
    return Image.fromarray(edges, "L")

# global variable
MAX_SEED = np.iinfo(np.int32).max
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if str(device).__contains__("cuda") else torch.float32
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "Watercolor"
enable_lcm_arg = False

# download checkpoints
from huggingface_hub import hf_hub_download

hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints")
hf_hub_download(
    repo_id="InstantX/InstantID",
    filename="ControlNetModel/diffusion_pytorch_model.safetensors",
    local_dir="./checkpoints",
)
hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints")

# Load face encoder
app = FaceAnalysis(
    name="antelopev2",
    root="./",
    providers=["CPUExecutionProvider"],
)
app.prepare(ctx_id=0, det_size=(640, 640))

depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")

# Path to InstantID models
face_adapter = f"./checkpoints/ip-adapter.bin"
controlnet_path = f"./checkpoints/ControlNetModel"

# Load pipeline face ControlNetModel
controlnet_identitynet = ControlNetModel.from_pretrained(
    controlnet_path, torch_dtype=dtype
)

# controlnet-pose/canny/depth
controlnet_pose_model = "thibaud/controlnet-openpose-sdxl-1.0"
controlnet_canny_model = "diffusers/controlnet-canny-sdxl-1.0"
controlnet_depth_model = "diffusers/controlnet-depth-sdxl-1.0-small"

controlnet_pose = ControlNetModel.from_pretrained(
    controlnet_pose_model, torch_dtype=dtype
).to(device)
controlnet_canny = ControlNetModel.from_pretrained(
    controlnet_canny_model, torch_dtype=dtype
).to(device)
controlnet_depth = ControlNetModel.from_pretrained(
    controlnet_depth_model, torch_dtype=dtype
).to(device)

controlnet_map = {
    "pose": controlnet_pose,
    "canny": controlnet_canny,
    "depth": controlnet_depth,
}
controlnet_map_fn = {
    "pose": openpose,
    "canny": get_canny_image,
    "depth": get_depth_map,
}

pretrained_model_name_or_path = "wangqixun/YamerMIX_v8"

pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
    pretrained_model_name_or_path,
    controlnet=[controlnet_identitynet],
    torch_dtype=dtype,
    safety_checker=None,
    feature_extractor=None,
).to(device)

pipe.scheduler = diffusers.EulerDiscreteScheduler.from_config(
    pipe.scheduler.config
)

pipe.load_ip_adapter_instantid(face_adapter)
# load and disable LCM
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")
pipe.disable_lora()

def toggle_lcm_ui(value):
    if value:
        return (
            gr.update(minimum=0, maximum=100, step=1, value=5),
            gr.update(minimum=0.1, maximum=20.0, step=0.1, value=1.5),
        )
    else:
        return (
            gr.update(minimum=5, maximum=100, step=1, value=30),
            gr.update(minimum=0.1, maximum=20.0, step=0.1, value=5),
        )

def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed

def remove_tips():
    return gr.update(visible=False)

def get_example():
    case = [
        [
            "./examples/yann-lecun_resize.jpg",
            None,
            "a man",
            "Snow",
            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
        ],
        [
            "./examples/musk_resize.jpeg",
            "./examples/poses/pose2.jpg",
            "a man flying in the sky in Mars",
            "Mars",
            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
        ],
        [
            "./examples/sam_resize.png",
            "./examples/poses/pose4.jpg",
            "a man doing a silly pose wearing a suite",
            "Jungle",
            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, gree",
        ],
        [
            "./examples/schmidhuber_resize.png",
            "./examples/poses/pose3.jpg",
            "a man sit on a chair",
            "Neon",
            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
        ],
        [
            "./examples/kaifu_resize.png",
            "./examples/poses/pose.jpg",
            "a man",
            "Vibrant Color",
            "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
        ],
    ]
    return case

def run_for_examples(face_file, pose_file, prompt, style, negative_prompt):
    return generate_image(
        face_file,
        pose_file,
        prompt,
        negative_prompt,
        style,
        20,  # num_steps
        0.8,  # identitynet_strength_ratio
        0.8,  # adapter_strength_ratio
        0.4,  # pose_strength
        0.3,  # canny_strength
        0.5,  # depth_strength
        ["pose", "canny"],  # controlnet_selection
        5.0,  # guidance_scale
        42,  # seed
        "EulerDiscreteScheduler",  # scheduler
        False,  # enable_LCM
        True,  # enable_Face_Region
    )

def convert_from_cv2_to_image(img: np.ndarray) -> Image:
    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))

def convert_from_image_to_cv2(img: Image) -> np.ndarray:
    return cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)

def resize_img(
    input_image,
    max_side=1280,
    min_side=1024,
    size=None,
    pad_to_max_side=False,
    mode=PIL.Image.BILINEAR,
    base_pixel_number=64,
):
    w, h = input_image.size
    if size is not None:
        w_resize_new, h_resize_new = size
    else:
        ratio = min_side / min(h, w)
        w, h = round(ratio * w), round(ratio * h)
        ratio = max_side / max(h, w)
        input_image = input_image.resize([round(ratio * w), round(ratio * h)], mode)
        w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
        h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
    input_image = input_image.resize([w_resize_new, h_resize_new], mode)

    if pad_to_max_side:
        res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
        offset_x = (max_side - w_resize_new) // 2
        offset_y = (max_side - h_resize_new) // 2
        res[
            offset_y : offset_y + h_resize_new, offset_x : offset_x + w_resize_new
        ] = np.array(input_image)
        input_image = Image.fromarray(res)
    return input_image

def apply_style(
    style_name: str, positive: str, negative: str = ""
) -> tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    return p.replace("{prompt}", positive), n + " " + negative

@spaces.GPU
def generate_image(
    face_image_path,
    pose_image_path,
    prompt,
    negative_prompt,
    style_name,
    num_steps,
    identitynet_strength_ratio,
    adapter_strength_ratio,
    pose_strength,
    canny_strength,
    depth_strength,
    controlnet_selection,
    guidance_scale,
    seed,
    scheduler,
    enable_LCM,
    enhance_face_region,
    progress=gr.Progress(track_tqdm=True),
):

    if enable_LCM:
        pipe.scheduler = diffusers.LCMScheduler.from_config(pipe.scheduler.config)
        pipe.enable_lora()
    else:
        pipe.disable_lora()
        scheduler_class_name = scheduler.split("-")[0]

        add_kwargs = {}
        if len(scheduler.split("-")) > 1:
            add_kwargs["use_karras_sigmas"] = True
        if len(scheduler.split("-")) > 2:
            add_kwargs["algorithm_type"] = "sde-dpmsolver++"
        scheduler = getattr(diffusers, scheduler_class_name)
        pipe.scheduler = scheduler.from_config(pipe.scheduler.config, **add_kwargs)

    if face_image_path is None:
        raise gr.Error(
            f"Cannot find any input face image! Please upload the face image"
        )

    if prompt is None:
        prompt = "a person"

    # apply the style template
    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)

    face_image = load_image(face_image_path)
    face_image = resize_img(face_image, max_side=1024)
    face_image_cv2 = convert_from_image_to_cv2(face_image)
    height, width, _ = face_image_cv2.shape

    # Extract face features
    face_info = app.get(face_image_cv2)

    if len(face_info) == 0:
        raise gr.Error(
            f"Unable to detect a face in the image. Please upload a different photo with a clear face."
        )

    face_info = sorted(
        face_info,
        key=lambda x: (x["bbox"][2] - x["bbox"][0]) * x["bbox"][3] - x["bbox"][1],
    )[
        -1
    ]  # only use the maximum face
    face_emb = face_info["embedding"]
    face_kps = draw_kps(convert_from_cv2_to_image(face_image_cv2), face_info["kps"])
    img_controlnet = face_image
    if pose_image_path is not None:
        pose_image = load_image(pose_image_path)
        pose_image = resize_img(pose_image, max_side=1024)
        img_controlnet = pose_image
        pose_image_cv2 = convert_from_image_to_cv2(pose_image)

        face_info = app.get(pose_image_cv2)

        if len(face_info) == 0:
            raise gr.Error(
                f"Cannot find any face in the reference image! Please upload another person image"
            )

        face_info = face_info[-1]
        face_kps = draw_kps(pose_image, face_info["kps"])

        width, height = face_kps.size

    if enhance_face_region:
        control_mask = np.zeros([height, width, 3])
        x1, y1, x2, y2 = face_info["bbox"]
        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
        control_mask[y1:y2, x1:x2] = 255
        control_mask = Image.fromarray(control_mask.astype(np.uint8))
    else:
        control_mask = None

    if len(controlnet_selection) > 0:
        controlnet_scales = {
            "pose": pose_strength,
            "canny": canny_strength,
            "depth": depth_strength,
        }
        pipe.controlnet = MultiControlNetModel(
            [controlnet_identitynet]
            + [controlnet_map[s] for s in controlnet_selection]
        )
        control_scales = [float(identitynet_strength_ratio)] + [
            controlnet_scales[s] for s in controlnet_selection
        ]
        control_images = [face_kps] + [
            controlnet_map_fn[s](img_controlnet).resize((width, height))
            for s in controlnet_selection
        ]
    else:
        pipe.controlnet = controlnet_identitynet
        control_scales = float(identitynet_strength_ratio)
        control_images = face_kps

    generator = torch.Generator(device=device).manual_seed(seed)

    print("Start inference...")
    print(f"[Debug] Prompt: {prompt}, \n[Debug] Neg Prompt: {negative_prompt}")

    pipe.set_ip_adapter_scale(adapter_strength_ratio)
    images = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        image_embeds=face_emb,
        image=control_images,
        control_mask=control_mask,
        controlnet_conditioning_scale=control_scales,
        num_inference_steps=num_steps,
        guidance_scale=guidance_scale,
        height=height,
        width=width,
        generator=generator,
    ).images

    return images[0], gr.update(visible=True)

# Description
title = r"""
<h1 align="center">InstantID: Zero-shot Identity-Preserving Generation in Seconds</h1>
"""

description = r"""
<b>Official 🤗 Gradio demo</b> for <a href='https://github.com/InstantID/InstantID' target='_blank'><b>InstantID: Zero-shot Identity-Preserving Generation in Seconds</b></a>.<br>

How to use:<br>
1. Upload an image with a face. For images with multiple faces, we will only detect the largest face. Ensure the face is not too small and is clearly visible without significant obstructions or blurring.
2. (Optional) You can upload another image as a reference for the face pose. If you don't, we will use the first detected face image to extract facial landmarks. If you use a cropped face at step 1, it is recommended to upload it to define a new face pose.
3. (Optional) You can select multiple ControlNet models to control the generation process. The default is to use the IdentityNet only. The ControlNet models include pose skeleton, canny, and depth. You can adjust the strength of each ControlNet model to control the generation process.
4. Enter a text prompt, as done in normal text-to-image models.
5. Click the <b>Submit</b> button to begin customization.
6. Share your customized photo with your friends and enjoy! 😊"""

article = r"""
---
📝 **Citation**
<br>
If our work is helpful for your research or applications, please cite us via:
```bibtex
@article{wang2024instantid,
title={InstantID: Zero-shot Identity-Preserving Generation in Seconds},
author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony},
journal={arXiv preprint arXiv:2401.07519},
year={2024}
}
```
📧 **Contact**
<br>
If you have any questions, please feel free to open an issue or directly reach us out at <b>haofanwang.ai@gmail.com</b>.
"""

tips = r"""
### Usage tips of InstantID
1. If you're not satisfied with the similarity, try increasing the weight of "IdentityNet Strength" and "Adapter Strength."    
2. If you feel that the saturation is too high, first decrease the Adapter strength. If it remains too high, then decrease the IdentityNet strength.
3. If you find that text control is not as expected, decrease Adapter strength.
4. If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model.
"""

css = """
.gradio-container {width: 85% !important}
"""
with gr.Blocks(css=css) as demo:
    # description
    gr.Markdown(title)
    gr.Markdown(description)

    with gr.Row():
        with gr.Column():
            with gr.Row(equal_height=True):
                # upload face image
                face_file = gr.Image(
                    label="Upload a photo of your face", type="filepath"
                )
                # optional: upload a reference pose image
                pose_file = gr.Image(
                    label="Upload a reference pose image (Optional)",
                    type="filepath",
                )

            # prompt
            prompt = gr.Textbox(
                label="Prompt",
                info="Give simple prompt is enough to achieve good face fidelity",
                placeholder="A photo of a person",
                value="",
            )

            submit = gr.Button("Submit", variant="primary")
            enable_LCM = gr.Checkbox(
                label="Enable Fast Inference with LCM", value=enable_lcm_arg,
                info="LCM speeds up the inference step, the trade-off is the quality of the generated image. It performs better with portrait face images rather than distant faces",
            )
            style = gr.Dropdown(
                label="Style template",
                choices=STYLE_NAMES,
                value=DEFAULT_STYLE_NAME,
            )

            # strength
            identitynet_strength_ratio = gr.Slider(
                label="IdentityNet strength (for fidelity)",
                minimum=0,
                maximum=1.5,
                step=0.05,
                value=0.80,
            )
            adapter_strength_ratio = gr.Slider(
                label="Image adapter strength (for detail)",
                minimum=0,
                maximum=1.5,
                step=0.05,
                value=0.80,
            )
            with gr.Accordion("Controlnet"):
                controlnet_selection = gr.CheckboxGroup(
                    ["pose", "canny", "depth"], label="Controlnet", value=["pose"],
                    info="Use pose for skeleton inference, canny for edge detection, and depth for depth map estimation. You can try all three to control the generation process"
                )
                pose_strength = gr.Slider(
                    label="Pose strength",
                    minimum=0,
                    maximum=1.5,
                    step=0.05,
                    value=0.40,
                )
                canny_strength = gr.Slider(
                    label="Canny strength",
                    minimum=0,
                    maximum=1.5,
                    step=0.05,
                    value=0.40,
                )
                depth_strength = gr.Slider(
                    label="Depth strength",
                    minimum=0,
                    maximum=1.5,
                    step=0.05,
                    value=0.40,
                )
            with gr.Accordion(open=False, label="Advanced Options"):
                negative_prompt = gr.Textbox(
                    label="Negative Prompt",
                    placeholder="low quality",
                    value="(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green",
                )
                num_steps = gr.Slider(
                    label="Number of sample steps",
                    minimum=1,
                    maximum=100,
                    step=1,
                    value=5 if enable_lcm_arg else 30,
                )
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.1,
                    maximum=20.0,
                    step=0.1,
                    value=0.0 if enable_lcm_arg else 5.0,
                )
                seed = gr.Slider(
                    label="Seed",
                    minimum=0,
                    maximum=MAX_SEED,
                    step=1,
                    value=42,
                )
                schedulers = [
                    "DEISMultistepScheduler",
                    "HeunDiscreteScheduler",
                    "EulerDiscreteScheduler",
                    "DPMSolverMultistepScheduler",
                    "DPMSolverMultistepScheduler-Karras",
                    "DPMSolverMultistepScheduler-Karras-SDE",
                ]
                scheduler = gr.Dropdown(
                    label="Schedulers",
                    choices=schedulers,
                    value="EulerDiscreteScheduler",
                )
                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                enhance_face_region = gr.Checkbox(label="Enhance non-face region", value=True)

        with gr.Column(scale=1):
            gallery = gr.Image(label="Generated Images")
            usage_tips = gr.Markdown(
                label="InstantID Usage Tips", value=tips, visible=False
            )

        submit.click(
            fn=remove_tips,
            outputs=usage_tips,
        ).then(
            fn=randomize_seed_fn,
            inputs=[seed, randomize_seed],
            outputs=seed,
            queue=False,
            api_name=False,
        ).then(
            fn=generate_image,
            inputs=[
                face_file,
                pose_file,
                prompt,
                negative_prompt,
                style,
                num_steps,
                identitynet_strength_ratio,
                adapter_strength_ratio,
                pose_strength,
                canny_strength,
                depth_strength,
                controlnet_selection,
                guidance_scale,
                seed,
                scheduler,
                enable_LCM,
                enhance_face_region,
            ],
            outputs=[gallery, usage_tips],
        )

        enable_LCM.input(
            fn=toggle_lcm_ui,
            inputs=[enable_LCM],
            outputs=[num_steps, guidance_scale],
            queue=False,
        )

    gr.Examples(
        examples=get_example(),
        inputs=[face_file, pose_file, prompt, style, negative_prompt],
        fn=run_for_examples,
        outputs=[gallery, usage_tips],
        cache_examples=True,
    )

    gr.Markdown(article)

demo.queue(api_open=False)
demo.launch()