File size: 9,938 Bytes
0a9bdfb
 
 
 
 
 
4a16e03
0a9bdfb
 
 
 
 
 
de2727e
0a9bdfb
 
 
 
 
 
ab6505a
0a9bdfb
9852d5d
 
 
0a9bdfb
 
3c0f460
 
 
0a9bdfb
3c0f460
 
 
0a9bdfb
 
3c0f460
 
 
 
0a9bdfb
 
 
 
 
 
 
 
ab6505a
64c7f5d
c30ec73
 
0a9bdfb
768f6bf
0a9bdfb
 
 
 
 
 
 
 
 
 
 
 
 
 
de2727e
 
0a9bdfb
ab6505a
0a9bdfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2d1f579
 
 
0a9bdfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4a16e03
 
0a9bdfb
 
 
 
 
 
 
de2727e
0a9bdfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e37e314
4a16e03
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0a9bdfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import os
from datetime import datetime
from pathlib import Path
import torch
from diffusers import AutoencoderKL, DDIMScheduler
from einops import repeat
from omegaconf import OmegaConf, DictConfig
from PIL import Image
from torchvision import transforms
from transformers import CLIPVisionModelWithProjection
import torch.nn.functional as F
import gc
from huggingface_hub import hf_hub_download
import gradio as gr

from musepose.models.pose_guider import PoseGuider
from musepose.models.unet_2d_condition import UNet2DConditionModel
from musepose.models.unet_3d import UNet3DConditionModel
from musepose.pipelines.pipeline_pose2vid_long import Pose2VideoPipeline
from musepose.utils.util import get_fps, read_frames, save_videos_grid
from downloading_weights import download_models

# ZeroGPU
import spaces


class MusePoseInference:
    def __init__(self,
                 model_dir,
                 output_dir):
        self.image_gen_model_paths = {
            "pretrained_base_model": os.path.join(model_dir, "sd-image-variations-diffusers"),
            "pretrained_vae": os.path.join(model_dir, "sd-vae-ft-mse"),
            "image_encoder": os.path.join(model_dir, "image_encoder"),
        }
        self.musepose_model_paths = {
            "denoising_unet": os.path.join(model_dir, "MusePose", "denoising_unet.pth"),
            "reference_unet": os.path.join(model_dir, "MusePose", "reference_unet.pth"),
            "pose_guider": os.path.join(model_dir, "MusePose", "pose_guider.pth"),
            "motion_module": os.path.join(model_dir, "MusePose", "motion_module.pth"),
        }
        self.inference_config_path = os.path.join("configs", "inference_v2.yaml")
        self.vae = None
        self.reference_unet = None
        self.denoising_unet = None
        self.pose_guider = None
        self.image_enc = None
        self.pipe = None
        self.model_dir = model_dir
        self.output_dir = os.path.join(output_dir, "musepose_inference")
        if not os.path.exists(self.output_dir):
            os.makedirs(self.output_dir)

    @spaces.GPU(duration=180)
    def infer_musepose(
        self,
        ref_image_path: str,
        pose_video_path: str,
        weight_dtype: str,
        W: int,
        H: int,
        L: int,
        S: int,
        O: int,
        cfg: float,
        seed: int,
        steps: int,
        fps: int,
        skip: int,
        gradio_progress=gr.Progress()
    ):
        download_models(model_dir=self.model_dir)
        print(f"Model Paths: {self.musepose_model_paths}\n{self.image_gen_model_paths}\n{self.inference_config_path}")
        print(f"Input Image Path: {ref_image_path}")
        print(f"Pose Video Path: {pose_video_path}")
        print(f"Dtype: {weight_dtype}")
        print(f"Width: {W}")
        print(f"Height: {H}")
        print(f"Video Frame Length: {L}")
        print(f"VIDEO SLICE FRAME LENGTH:: {S}")
        print(f"VIDEO SLICE OVERLAP_FRAME NUMBER: {O}")
        print(f"CFG: {cfg}")
        print(f"Seed: {seed}")
        print(f"Steps: {steps}")
        print(f"FPS: {fps}")
        print(f"Skip: {skip}")

        output_filename = f"output_temp"
        output_path = os.path.abspath(os.path.join(self.output_dir, f'{output_filename}.mp4'))
        output_path_demo = os.path.abspath(os.path.join(self.output_dir, f'{output_filename}_demo.mp4'))

        if weight_dtype == "fp16":
            weight_dtype = torch.float16
        else:
            weight_dtype = torch.float32

        inference_config_path = self.inference_config_path
        infer_config = OmegaConf.load(inference_config_path)

        sched_kwargs = OmegaConf.to_container(infer_config.noise_scheduler_kwargs)
        scheduler = DDIMScheduler(**sched_kwargs)

        generator = torch.manual_seed(seed)

        width, height = W, H

        self.init_model(weight_dtype=weight_dtype, infer_config=infer_config)

        self.pipe = Pose2VideoPipeline(
            vae=self.vae,
            image_encoder=self.image_enc,
            reference_unet=self.reference_unet,
            denoising_unet=self.denoising_unet,
            pose_guider=self.pose_guider,
            scheduler=scheduler,
            gradio_progress=gradio_progress
        )
        self.pipe = self.pipe.to("cuda", dtype=weight_dtype)

        print("image: ", ref_image_path, "pose_video: ", pose_video_path)

        ref_image_pil = Image.open(ref_image_path).convert("RGB")

        pose_list = []
        pose_tensor_list = []
        pose_images = read_frames(pose_video_path)
        src_fps = get_fps(pose_video_path)
        print(f"pose video has {len(pose_images)} frames, with {src_fps} fps")
        L = min(L, len(pose_images))
        pose_transform = transforms.Compose(
            [transforms.Resize((height, width)), transforms.ToTensor()]
        )
        original_width, original_height = 0, 0

        pose_images = pose_images[::skip + 1]
        print("processing length:", len(pose_images))
        src_fps = src_fps // (skip + 1)
        print("fps", src_fps)
        L = L // ((skip + 1))

        for pose_image_pil in pose_images[: L]:
            pose_tensor_list.append(pose_transform(pose_image_pil))
            pose_list.append(pose_image_pil)
            original_width, original_height = pose_image_pil.size
            pose_image_pil = pose_image_pil.resize((width, height))

        # repeart the last segment
        last_segment_frame_num = (L - S) % (S - O)
        repeart_frame_num = (S - O - last_segment_frame_num) % (S - O)
        for i in range(repeart_frame_num):
            pose_list.append(pose_list[-1])
            pose_tensor_list.append(pose_tensor_list[-1])

        ref_image_tensor = pose_transform(ref_image_pil)  # (c, h, w)
        ref_image_tensor = ref_image_tensor.unsqueeze(1).unsqueeze(0)  # (1, c, 1, h, w)
        ref_image_tensor = repeat(ref_image_tensor, "b c f h w -> b c (repeat f) h w", repeat=L)

        pose_tensor = torch.stack(pose_tensor_list, dim=0)  # (f, c, h, w)
        pose_tensor = pose_tensor.transpose(0, 1)
        pose_tensor = pose_tensor.unsqueeze(0)

        video = self.pipe(
            ref_image_pil,
            pose_list,
            width,
            height,
            len(pose_list),
            steps,
            cfg,
            generator=generator,
            context_frames=S,
            context_stride=1,
            context_overlap=O,
        ).videos

        result = self.scale_video(video[:, :, :L], original_width, original_height)
        save_videos_grid(
            result,
            output_path,
            n_rows=1,
            fps=src_fps if fps is None or fps < 0 else fps,
        )

        video = torch.cat([ref_image_tensor, pose_tensor[:, :, :L], video[:, :, :L]], dim=0)
        video = self.scale_video(video, original_width, original_height)
        save_videos_grid(
            video,
            output_path_demo,
            n_rows=3,
            fps=src_fps if fps is None or fps < 0 else fps,
        )
        return output_path, output_path_demo

    @spaces.GPU(duration=120)
    def init_model(self,
                   weight_dtype: torch.dtype,
                   infer_config: DictConfig
                   ):
        if self.vae is None:
            self.vae = AutoencoderKL.from_pretrained(
                self.image_gen_model_paths["pretrained_vae"],
            ).to("cuda", dtype=weight_dtype)

        if self.reference_unet is None:
            self.reference_unet = UNet2DConditionModel.from_pretrained(
                self.image_gen_model_paths["pretrained_base_model"],
                subfolder="unet",
            ).to(dtype=weight_dtype, device="cuda")
            self.reference_unet.load_state_dict(
                torch.load(self.musepose_model_paths["reference_unet"], map_location="cpu"),
            )

        if self.denoising_unet is None:
            self.denoising_unet = UNet3DConditionModel.from_pretrained_2d(
                Path(self.image_gen_model_paths["pretrained_base_model"]),
                Path(self.musepose_model_paths["motion_module"]),
                subfolder="unet",
                unet_additional_kwargs=infer_config.unet_additional_kwargs,
            ).to(dtype=weight_dtype, device="cuda")
            self.denoising_unet.load_state_dict(
                torch.load(self.musepose_model_paths["denoising_unet"], map_location="cpu"),
                strict=False,
            )

        if self.pose_guider is None:
            self.pose_guider = PoseGuider(320, block_out_channels=(16, 32, 96, 256)).to(
                dtype=weight_dtype, device="cuda"
            )
            self.pose_guider.load_state_dict(
                torch.load(self.musepose_model_paths["pose_guider"], map_location="cpu"),
            )

        if self.image_enc is None:
            self.image_enc = CLIPVisionModelWithProjection.from_pretrained(
                self.image_gen_model_paths["image_encoder"]
            ).to(dtype=weight_dtype, device="cuda")

    def release_vram(self):
        models = [
            'vae', 'reference_unet', 'denoising_unet',
            'pose_guider', 'image_enc', 'pipe'
        ]

        for model_name in models:
            model = getattr(self, model_name, None)
            if model is not None:
                del model
                setattr(self, model_name, None)

        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()

    @staticmethod
    def scale_video(video, width, height):
        video_reshaped = video.view(-1, *video.shape[2:])  # [batch*frames, channels, height, width]
        scaled_video = F.interpolate(video_reshaped, size=(height, width), mode='bilinear', align_corners=False)
        scaled_video = scaled_video.view(*video.shape[:2], scaled_video.shape[1], height,
                                         width)  # [batch, frames, channels, height, width]

        return scaled_video