diff options
Diffstat (limited to 'modules')
| -rw-r--r-- | modules/devices.py | 16 | ||||
| -rw-r--r-- | modules/interrogate.py | 142 | ||||
| -rw-r--r-- | modules/paths.py | 1 | ||||
| -rw-r--r-- | modules/shared.py | 8 | ||||
| -rw-r--r-- | modules/ui.py | 18 |
5 files changed, 177 insertions, 8 deletions
diff --git a/modules/devices.py b/modules/devices.py index 25008a04..30d30b99 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,12 +1,16 @@ import torch - # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility has_mps = getattr(torch, 'has_mps', False) +cpu = torch.device("cpu") + + def get_optimal_device(): - if torch.cuda.is_available(): - return torch.device("cuda") - if has_mps: - return torch.device("mps") - return torch.device("cpu") + if torch.cuda.is_available(): + return torch.device("cuda") + + if has_mps: + return torch.device("mps") + + return cpu diff --git a/modules/interrogate.py b/modules/interrogate.py new file mode 100644 index 00000000..ed97a58b --- /dev/null +++ b/modules/interrogate.py @@ -0,0 +1,142 @@ +import os
+import sys
+import traceback
+from collections import namedtuple
+import re
+
+import torch
+
+from PIL import Image
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+import modules.shared as shared
+from modules import devices, paths
+
+blip_image_eval_size = 384
+blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth'
+clip_model_name = 'ViT-L/14'
+
+Category = namedtuple("Category", ["name", "topn", "items"])
+
+re_topn = re.compile(r"\.top(\d+)\.")
+
+class InterrogateModels:
+ blip_model = None
+ clip_model = None
+ clip_preprocess = None
+ categories = None
+
+ def __init__(self, content_dir):
+ self.categories = []
+
+ if os.path.exists(content_dir):
+ for filename in os.listdir(content_dir):
+ m = re_topn.search(filename)
+ topn = 1 if m is None else int(m.group(1))
+
+ with open(os.path.join(content_dir, filename), "r", encoding="utf8") as file:
+ lines = [x.strip() for x in file.readlines()]
+
+ self.categories.append(Category(name=filename, topn=topn, items=lines))
+
+ def load_blip_model(self):
+ import models.blip
+
+ blip_model = models.blip.blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base', med_config=os.path.join(paths.paths["BLIP"], "configs", "med_config.json"))
+ blip_model.eval()
+
+ return blip_model
+
+ def load_clip_model(self):
+ import clip
+
+ model, preprocess = clip.load(clip_model_name)
+ model.eval()
+ model = model.to(shared.device)
+
+ return model, preprocess
+
+ def load(self):
+ if self.blip_model is None:
+ self.blip_model = self.load_blip_model()
+
+ self.blip_model = self.blip_model.to(shared.device)
+
+ if self.clip_model is None:
+ self.clip_model, self.clip_preprocess = self.load_clip_model()
+
+ self.clip_model = self.clip_model.to(shared.device)
+
+ def unload(self):
+ if not shared.opts.interrogate_keep_models_in_memory:
+ if self.clip_model is not None:
+ self.clip_model = self.clip_model.to(devices.cpu)
+
+ if self.blip_model is not None:
+ self.blip_model = self.blip_model.to(devices.cpu)
+
+
+ def rank(self, image_features, text_array, top_count=1):
+ import clip
+
+ top_count = min(top_count, len(text_array))
+ text_tokens = clip.tokenize([text for text in text_array]).cuda()
+ with torch.no_grad():
+ text_features = self.clip_model.encode_text(text_tokens).float()
+ text_features /= text_features.norm(dim=-1, keepdim=True)
+
+ similarity = torch.zeros((1, len(text_array))).to(shared.device)
+ for i in range(image_features.shape[0]):
+ similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
+ similarity /= image_features.shape[0]
+
+ top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)
+ return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]
+
+
+ def generate_caption(self, pil_image):
+ gpu_image = transforms.Compose([
+ transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
+ transforms.ToTensor(),
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
+ ])(pil_image).unsqueeze(0).to(shared.device)
+
+ with torch.no_grad():
+ caption = self.blip_model.generate(gpu_image, sample=False, num_beams=shared.opts.interrogate_clip_num_beams, min_length=shared.opts.interrogate_clip_min_length, max_length=shared.opts.interrogate_clip_max_length)
+
+ return caption[0]
+
+ def interrogate(self, pil_image):
+ res = None
+
+ try:
+ self.load()
+
+ caption = self.generate_caption(pil_image)
+ res = caption
+
+ images = self.clip_preprocess(pil_image).unsqueeze(0).to(shared.device)
+
+ with torch.no_grad():
+ image_features = self.clip_model.encode_image(images).float()
+
+ image_features /= image_features.norm(dim=-1, keepdim=True)
+
+ if shared.opts.interrogate_use_builtin_artists:
+ artist = self.rank(image_features, ["by " + artist.name for artist in shared.artist_db.artists])[0]
+
+ res += ", " + artist[0]
+
+ for name, topn, items in self.categories:
+ matches = self.rank(image_features, items, top_count=topn)
+ for match, score in matches:
+ res += ", " + match
+
+ except Exception:
+ print(f"Error interrogating", file=sys.stderr)
+ print(traceback.format_exc(), file=sys.stderr)
+
+ self.unload()
+
+ return res
diff --git a/modules/paths.py b/modules/paths.py index 130aecb9..97c17a98 100644 --- a/modules/paths.py +++ b/modules/paths.py @@ -18,6 +18,7 @@ path_dirs = [ (sd_path, 'ldm', 'Stable Diffusion'),
(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers'),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer'),
+ (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP'),
]
paths = {}
diff --git a/modules/shared.py b/modules/shared.py index 74b0ad89..9eeb64e3 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -11,6 +11,7 @@ import modules.artists from modules.paths import script_path, sd_path
from modules.devices import get_optimal_device
import modules.styles
+import modules.interrogate
config_filename = "config.json"
@@ -77,6 +78,8 @@ artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.c styles_filename = os.path.join(script_path, 'styles.csv')
prompt_styles = modules.styles.load_styles(styles_filename)
+interrogator = modules.interrogate.InterrogateModels("interrogate")
+
face_restorers = []
class Options:
@@ -123,6 +126,11 @@ class Options: "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job. Broken in PyCharm console."),
"face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+ "interrogate_keep_models_in_memory": OptionInfo(True, "Interrogate: keep models in VRAM"),
+ "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
+ "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+ "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum descripton length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+ "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum descripton length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
}
def __init__(self):
diff --git a/modules/ui.py b/modules/ui.py index 032c20ff..ebc1ae63 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -242,9 +242,14 @@ def add_style(style_name, text): return [update, update]
+def interrogate(image):
+ prompt = shared.interrogator.interrogate(image)
+
+ return gr_show(True) if prompt is None else prompt
+
def create_ui(txt2img, img2img, run_extras, run_pnginfo):
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
- with gr.Row():
+ with gr.Row(elem_id="toprow"):
txt2img_prompt = gr.Textbox(label="Prompt", elem_id="txt2img_prompt", show_label=False, placeholder="Prompt", lines=1)
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="txt2img_negative_prompt", show_label=False, placeholder="Negative prompt", lines=1)
txt2img_prompt_style = gr.Dropdown(label="Style", show_label=False, elem_id="style_index", choices=[k for k, v in shared.prompt_styles.items()], value=next(iter(shared.prompt_styles.keys())), visible=len(shared.prompt_styles) > 1)
@@ -365,10 +370,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): )
with gr.Blocks(analytics_enabled=False) as img2img_interface:
- with gr.Row():
+ with gr.Row(elem_id="toprow"):
img2img_prompt = gr.Textbox(label="Prompt", elem_id="img2img_prompt", show_label=False, placeholder="Prompt", lines=1)
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="img2img_negative_prompt", show_label=False, placeholder="Negative prompt", lines=1)
img2img_prompt_style = gr.Dropdown(label="Style", show_label=False, elem_id="style_index", choices=[k for k, v in shared.prompt_styles.items()], value=next(iter(shared.prompt_styles.keys())), visible=len(shared.prompt_styles) > 1)
+ img2img_interrogate = gr.Button('Interrogate', elem_id="img2img_interrogate", variant='primary')
submit = gr.Button('Generate', elem_id="img2img_generate", variant='primary')
check_progress = gr.Button('Check progress', elem_id="check_progress", visible=False)
@@ -461,6 +467,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): inpaint_full_res: gr_show(is_inpaint),
inpainting_mask_invert: gr_show(is_inpaint),
denoising_strength_change_factor: gr_show(is_loopback),
+ img2img_interrogate: gr_show(not is_inpaint),
}
switch_mode.change(
@@ -480,6 +487,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): inpaint_full_res,
inpainting_mask_invert,
denoising_strength_change_factor,
+ img2img_interrogate,
]
)
@@ -540,6 +548,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): img2img_prompt.submit(**img2img_args)
submit.click(**img2img_args)
+ img2img_interrogate.click(
+ fn=interrogate,
+ inputs=[init_img],
+ outputs=[img2img_prompt],
+ )
+
check_progress.click(
fn=check_progress_call,
show_progress=False,
|