File size: 7,303 Bytes
c7db14f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
#!/usr/bin/env python
# coding=utf-8
# Copyright (C) 2025 AIDC-AI
#
# Licensed under the Apache License, Version 2.0 (the "License"); 
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at 
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software 
# distributed under the License is distributed on an "AS IS" BASIS, 
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
# See the License for the specific language governing permissions and 
# limitations under the License.

from typing import Optional, Union, List, Dict, Any

import math
import os
import torch
import torch.nn as nn
from diffusers import DiffusionPipeline, EulerDiscreteScheduler, SchedulerMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import logging
from PIL import Image

from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker

def get_noise(
    num_samples: int,
    channel: int,
    height: int,
    width: int,
    device: torch.device,
    dtype: torch.dtype,
    seed: int,
):
    return torch.randn(
        num_samples,
        channel,
        # allow for packing
        2 * math.ceil(height / 16),
        2 * math.ceil(width / 16),
        device=device,
        dtype=dtype,
        generator=torch.Generator(device=device).manual_seed(seed),
    )

class ChatsSDXLPipeline(DiffusionPipeline, ConfigMixin):

    @register_to_config
    def __init__(
        self,
        unet_win: nn.Module,
        unet_lose: nn.Module,
        text_encoder: CLIPTextModel,
        text_encoder_two: CLIPTextModelWithProjection,
        tokenizer: CLIPTokenizer,
        tokenizer_two: CLIPTokenizer,
        vae: AutoencoderKL,
        scheduler: SchedulerMixin,
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor
    ):
        super().__init__()

        self.register_modules(
            unet_win=unet_win,
            unet_lose=unet_lose,
            text_encoder=text_encoder,
            text_encoder_two=text_encoder_two,
            tokenizer=tokenizer,
            tokenizer_two=tokenizer_two,
            vae=vae,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor
        )

    
    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Union[str, os.PathLike],
        **kwargs,
    ) -> "ChatsSDXLPipeline":

        return super().from_pretrained(pretrained_model_name_or_path, **kwargs)

    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
        super().save_pretrained(save_directory)

    @torch.no_grad()
    def encode_text(self, tokenizers, text_encoders, prompt):
      prompt_embeds_list = []

      with torch.no_grad():
          for tokenizer, text_encoder in zip(tokenizers, text_encoders):
              text_inputs = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt",)    
              text_input_ids = text_inputs.input_ids
              prompt_embeds = text_encoder(text_input_ids.to(self.unet_win.device), output_hidden_states=True)
              pooled_prompt_embeds = prompt_embeds[0]
              prompt_embeds = prompt_embeds.hidden_states[-2]
              prompt_embeds_list.append(prompt_embeds)
      
      prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
      prompt_embeds = prompt_embeds.to(dtype=text_encoders[-1].dtype, device=text_encoders[-1].device)

      return prompt_embeds, pooled_prompt_embeds

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        latents: torch.FloatTensor = None,
        height: int = 1024,
        width: int = 1024,
        seed: int = 0,
        alpha: float=0.5
    ):  
        if isinstance(prompt, str):
            prompt = [prompt]

        device = self.unet_win.device

        tokenizers = [self.tokenizer, self.tokenizer_two]
        text_encoders = [self.text_encoder, self.text_encoder_two]

        prompt_embeds, pooled_prompt_embeds = self.encode_text(tokenizers, text_encoders, prompt)
        negative_prompt_embeds, negative_pooled_prompt_embeds = self.encode_text(tokenizers, text_encoders, "")

        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps = self.scheduler.timesteps

        bs = len(prompt)
        channel = self.vae.config.latent_channels
        height = 16 * (height // 16)
        width = 16 * (width // 16)

        # prepare input
        latents = get_noise(
            bs,
            channel,
            height,
            width,
            device=device,
            dtype=self.unet_win.dtype,
            seed=seed,
        )
        latents = latents * self.scheduler.init_noise_sigma
        
        add_time_ids = torch.tensor([height, width, 0, 0, height, width], dtype=latents.dtype, device=device)[None, :].repeat(latents.size(0), 1)

        for i, t in enumerate(timesteps):
            latent_model_input = self.scheduler.scale_model_input(latents, t)

            added_cond_kwargs_win = {"text_embeds": pooled_prompt_embeds, "time_ids": add_time_ids}
            added_cond_kwargs_lose = {"text_embeds": pooled_prompt_embeds * (-alpha) + negative_pooled_prompt_embeds * (1. + alpha), "time_ids": add_time_ids}

            pred_win = self.unet_win(latent_model_input, t, encoder_hidden_states=prompt_embeds, added_cond_kwargs=added_cond_kwargs_win, return_dict=False)[0]
            pred_lose = self.unet_lose(latent_model_input, t, encoder_hidden_states=prompt_embeds * (-alpha) + negative_prompt_embeds * (1. + alpha), added_cond_kwargs=added_cond_kwargs_lose, return_dict=False)[0]

            noise_pred = pred_win + guidance_scale * (pred_win - pred_lose)
            latents = self.scheduler.step(noise_pred, t, latents, generator=None, return_dict=False)[0]

        x = latents.float()

        with torch.no_grad():
            with torch.autocast(device_type=device.type, dtype=torch.float32):
                if hasattr(self.vae.config, 'scaling_factor') and self.vae.config.scaling_factor is not None:
                    x = x / self.vae.config.scaling_factor
                if hasattr(self.vae.config, 'shift_factor') and self.vae.config.shift_factor is not None:
                    x = x + self.vae.config.shift_factor
                x = self.vae.decode(x, return_dict=False)[0]

        # bring into PIL format and save
        x = (x / 2 + 0.5).clamp(0, 1)
        x = x.cpu().permute(0, 2, 3, 1).float().numpy()
        images = (x * 255).round().astype("uint8")

        clip_input = self.feature_extractor(images=images, return_tensors="pt").to(self.device)
        filtered_images, has_nsfw_flags = self.safety_checker(images=images, clip_input=clip_input.pixel_values)

        return {"images": filtered_images, "nsfw_flags": has_nsfw_flags}