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Merge branch 'feature/progress-bar'

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# Conflicts:
#	fooocus_version.py
#	modules/async_worker.py
#	webui.py
This commit is contained in:
Manuel Schmid
2024-05-19 20:54:53 +02:00
parent 84a71d1ae4 5d8eb5235a
commit dd5a14ac7f
20 changed files with 598 additions and 151 deletions
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extras/censor.py
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import os
import numpy as np
import torch
from transformers import CLIPConfig, CLIPImageProcessor
import ldm_patched.modules.model_management as model_management
import modules.config
from extras.safety_checker.models.safety_checker import StableDiffusionSafetyChecker
from ldm_patched.modules.model_patcher import ModelPatcher
safety_checker_repo_root = os.path.join(os.path.dirname(__file__), 'safety_checker')
config_path = os.path.join(safety_checker_repo_root, "configs", "config.json")
preprocessor_config_path = os.path.join(safety_checker_repo_root, "configs", "preprocessor_config.json")
class Censor:
def __init__(self):
self.safety_checker_model: ModelPatcher | None = None
self.clip_image_processor: CLIPImageProcessor | None = None
self.load_device = torch.device('cpu')
self.offload_device = torch.device('cpu')
def init(self):
if self.safety_checker_model is None and self.clip_image_processor is None:
safety_checker_model = modules.config.downloading_safety_checker_model()
self.clip_image_processor = CLIPImageProcessor.from_json_file(preprocessor_config_path)
clip_config = CLIPConfig.from_json_file(config_path)
model = StableDiffusionSafetyChecker.from_pretrained(safety_checker_model, config=clip_config)
model.eval()
self.load_device = model_management.text_encoder_device()
self.offload_device = model_management.text_encoder_offload_device()
model.to(self.offload_device)
self.safety_checker_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device)
def censor(self, images: list | np.ndarray) -> list | np.ndarray:
self.init()
model_management.load_model_gpu(self.safety_checker_model)
single = False
if not isinstance(images, list) or isinstance(images, np.ndarray):
images = [images]
single = True
safety_checker_input = self.clip_image_processor(images, return_tensors="pt")
safety_checker_input.to(device=self.load_device)
checked_images, has_nsfw_concept = self.safety_checker_model.model(images=images,
clip_input=safety_checker_input.pixel_values)
checked_images = [image.astype(np.uint8) for image in checked_images]
if single:
checked_images = checked_images[0]
return checked_images
default_censor = Censor().censor
extras/safety_checker/configs/config.json
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{
"_name_or_path": "clip-vit-large-patch14/",
"architectures": [
"SafetyChecker"
],
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "clip",
"projection_dim": 768,
"text_config": {
"_name_or_path": "",
"add_cross_attention": false,
"architectures": null,
"attention_dropout": 0.0,
"bad_words_ids": null,
"bos_token_id": 0,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0.0,
"do_sample": false,
"dropout": 0.0,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": 2,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "quick_gelu",
"hidden_size": 768,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-05,
"length_penalty": 1.0,
"max_length": 20,
"max_position_embeddings": 77,
"min_length": 0,
"model_type": "clip_text_model",
"no_repeat_ngram_size": 0,
"num_attention_heads": 12,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": 12,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": 1,
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1.0,
"return_dict": true,
"return_dict_in_generate": false,
"sep_token_id": null,
"task_specific_params": null,
"temperature": 1.0,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": null,
"torchscript": false,
"transformers_version": "4.21.0.dev0",
"typical_p": 1.0,
"use_bfloat16": false,
"vocab_size": 49408
},
"text_config_dict": {
"hidden_size": 768,
"intermediate_size": 3072,
"num_attention_heads": 12,
"num_hidden_layers": 12
},
"torch_dtype": "float32",
"transformers_version": null,
"vision_config": {
"_name_or_path": "",
"add_cross_attention": false,
"architectures": null,
"attention_dropout": 0.0,
"bad_words_ids": null,
"bos_token_id": null,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": null,
"decoder_start_token_id": null,
"diversity_penalty": 0.0,
"do_sample": false,
"dropout": 0.0,
"early_stopping": false,
"encoder_no_repeat_ngram_size": 0,
"eos_token_id": null,
"exponential_decay_length_penalty": null,
"finetuning_task": null,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"hidden_act": "quick_gelu",
"hidden_size": 1024,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"image_size": 224,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 4096,
"is_decoder": false,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-05,
"length_penalty": 1.0,
"max_length": 20,
"min_length": 0,
"model_type": "clip_vision_model",
"no_repeat_ngram_size": 0,
"num_attention_heads": 16,
"num_beam_groups": 1,
"num_beams": 1,
"num_hidden_layers": 24,
"num_return_sequences": 1,
"output_attentions": false,
"output_hidden_states": false,
"output_scores": false,
"pad_token_id": null,
"patch_size": 14,
"prefix": null,
"problem_type": null,
"pruned_heads": {},
"remove_invalid_values": false,
"repetition_penalty": 1.0,
"return_dict": true,
"return_dict_in_generate": false,
"sep_token_id": null,
"task_specific_params": null,
"temperature": 1.0,
"tie_encoder_decoder": false,
"tie_word_embeddings": true,
"tokenizer_class": null,
"top_k": 50,
"top_p": 1.0,
"torch_dtype": null,
"torchscript": false,
"transformers_version": "4.21.0.dev0",
"typical_p": 1.0,
"use_bfloat16": false
},
"vision_config_dict": {
"hidden_size": 1024,
"intermediate_size": 4096,
"num_attention_heads": 16,
"num_hidden_layers": 24,
"patch_size": 14
}
}
extras/safety_checker/configs/preprocessor_config.json
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{
"crop_size": 224,
"do_center_crop": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "CLIPFeatureExtractor",
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"resample": 3,
"size": 224
}
extras/safety_checker/models/safety_checker.py
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# from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# 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.
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def cosine_distance(image_embeds, text_embeds):
normalized_image_embeds = nn.functional.normalize(image_embeds)
normalized_text_embeds = nn.functional.normalize(text_embeds)
return torch.mm(normalized_image_embeds, normalized_text_embeds.t())
class StableDiffusionSafetyChecker(PreTrainedModel):
config_class = CLIPConfig
main_input_name = "clip_input"
_no_split_modules = ["CLIPEncoderLayer"]
def __init__(self, config: CLIPConfig):
super().__init__(config)
self.vision_model = CLIPVisionModel(config.vision_config)
self.visual_projection = nn.Linear(config.vision_config.hidden_size, config.projection_dim, bias=False)
self.concept_embeds = nn.Parameter(torch.ones(17, config.projection_dim), requires_grad=False)
self.special_care_embeds = nn.Parameter(torch.ones(3, config.projection_dim), requires_grad=False)
self.concept_embeds_weights = nn.Parameter(torch.ones(17), requires_grad=False)
self.special_care_embeds_weights = nn.Parameter(torch.ones(3), requires_grad=False)
@torch.no_grad()
def forward(self, clip_input, images):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds).cpu().float().numpy()
cos_dist = cosine_distance(image_embeds, self.concept_embeds).cpu().float().numpy()
result = []
batch_size = image_embeds.shape[0]
for i in range(batch_size):
result_img = {"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
for concept_idx in range(len(special_cos_dist[0])):
concept_cos = special_cos_dist[i][concept_idx]
concept_threshold = self.special_care_embeds_weights[concept_idx].item()
result_img["special_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["special_scores"][concept_idx] > 0:
result_img["special_care"].append({concept_idx, result_img["special_scores"][concept_idx]})
adjustment = 0.01
for concept_idx in range(len(cos_dist[0])):
concept_cos = cos_dist[i][concept_idx]
concept_threshold = self.concept_embeds_weights[concept_idx].item()
result_img["concept_scores"][concept_idx] = round(concept_cos - concept_threshold + adjustment, 3)
if result_img["concept_scores"][concept_idx] > 0:
result_img["bad_concepts"].append(concept_idx)
result.append(result_img)
has_nsfw_concepts = [len(res["bad_concepts"]) > 0 for res in result]
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if torch.is_tensor(images) or torch.is_tensor(images[0]):
images[idx] = torch.zeros_like(images[idx]) # black image
else:
images[idx] = np.zeros(images[idx].shape) # black image
if any(has_nsfw_concepts):
logger.warning(
"Potential NSFW content was detected in one or more images. A black image will be returned instead."
" Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
@torch.no_grad()
def forward_onnx(self, clip_input: torch.Tensor, images: torch.Tensor):
pooled_output = self.vision_model(clip_input)[1] # pooled_output
image_embeds = self.visual_projection(pooled_output)
special_cos_dist = cosine_distance(image_embeds, self.special_care_embeds)
cos_dist = cosine_distance(image_embeds, self.concept_embeds)
# increase this value to create a stronger `nsfw` filter
# at the cost of increasing the possibility of filtering benign images
adjustment = 0.0
special_scores = special_cos_dist - self.special_care_embeds_weights + adjustment
# special_scores = special_scores.round(decimals=3)
special_care = torch.any(special_scores > 0, dim=1)
special_adjustment = special_care * 0.01
special_adjustment = special_adjustment.unsqueeze(1).expand(-1, cos_dist.shape[1])
concept_scores = (cos_dist - self.concept_embeds_weights) + special_adjustment
# concept_scores = concept_scores.round(decimals=3)
has_nsfw_concepts = torch.any(concept_scores > 0, dim=1)
images[has_nsfw_concepts] = 0.0 # black image
return images, has_nsfw_concepts