Merge remote-tracking branch 'upstream/development' into fix-prompts

README.md

@@ -2,7 +2,7 @@
 
 # InvokeAI: A Stable Diffusion Toolkit
 
-_Formally known as lstein/stable-diffusion_
+_Formerly known as lstein/stable-diffusion_
 
 ![project logo](docs/assets/logo.png)
 

docs/features/CLI.md

@@ -503,6 +503,16 @@ invoke> !search surreal
 This clears the search history from memory and disk. Be advised that
 this operation is irreversible and does not issue any warnings!
 
+Other ! Commands
+
+### !mask
+
+This command takes an image, a text prompt, and uses the `clipseg`
+algorithm to automatically generate a mask of the area that matches
+the text prompt. It is useful for debugging the text masking process
+prior to inpainting with the `--text_mask` argument. See
+[INPAINTING.md] for details.
+
 ## Command-line editing and completion
 
 The command-line offers convenient history tracking, editing, and

docs/features/INPAINTING.md

@@ -74,6 +74,60 @@ up at all!
 invoke> a baseball -I /path/to/breakfast.png -tm orange 0.6
 ~~~
 
+The `!mask` command may be useful for debugging problems with the
+text2mask feature. The syntax is `!mask /path/to/image.png -tm <text>
+<threshold>`
+
+It will generate three files:
+
+- The image with the selected area highlighted.
+- The image with the un-selected area highlighted.
+- The image with the selected area converted into a black and white
+  image according to the threshold level.
+
+Note that none of these images are intended to be used as the mask
+passed to invoke via `-M` and may give unexpected results if you try
+to use them this way. Instead, use `!mask` for testing that you are
+selecting the right mask area, and then do inpainting using the
+best selection term and threshold.
+
+Here is an example of how `!mask` works:
+
+```
+invoke> !mask ./test-pictures/curly.png -tm hair 0.5
+>> generating masks from ./test-pictures/curly.png
+>> Initializing clipseg model for text to mask inference
+Outputs:
+[941.1] outputs/img-samples/000019.curly.hair.deselected.png: !mask ./test-pictures/curly.png -tm hair 0.5
+[941.2] outputs/img-samples/000019.curly.hair.selected.png: !mask ./test-pictures/curly.png -tm hair 0.5
+[941.3] outputs/img-samples/000019.curly.hair.masked.png: !mask ./test-pictures/curly.png -tm hair 0.5
+```
+
+**Original image "curly.png"**
+<img src="../assets/outpainting/curly.png">
+
+**000019.curly.hair.selected.png**
+<img src="../assets/inpainting/000019.curly.hair.selected.png">
+
+**000019.curly.hair.deselected.png**
+<img src="../assets/inpainting/000019.curly.hair.deselected.png">
+
+**000019.curly.hair.masked.png**
+<img src="../assets/inpainting/000019.curly.hair.masked.png">
+
+It looks like we selected the hair pretty well at the 0.5 threshold
+(which is the default, so we didn't actually have to specify it), so
+let's have some fun:
+
+```
+invoke> medusa with cobras -I ./test-pictures/curly.png -tm hair 0.5 -C20
+>> loaded input image of size 512x512 from ./test-pictures/curly.png
+...
+Outputs:
+[946] outputs/img-samples/000024.801380492.png: "medusa with cobras" -s 50 -S 801380492 -W 512 -H 512 -C 20.0 -I ./test-pictures/curly.png -A k_lms -f 0.75
+```
+
+<img src="../assets/inpainting/000024.801380492.png">
 
 ### Inpainting is not changing the masked region enough!
 

docs/index.md

@@ -12,7 +12,7 @@ title: Home
 -->
 <div align="center" markdown>
 
-# ^^**InvokeAI: A Stable Diffusion Toolkit**^^ :tools: <br> <small>Formally known as lstein/stable-diffusion</small>
+# ^^**InvokeAI: A Stable Diffusion Toolkit**^^ :tools: <br> <small>Formerly known as lstein/stable-diffusion</small>
 
 ![project logo](assets/logo.png)
 

environment-mac.yml

@@ -57,7 +57,7 @@ dependencies:
       - -e git+https://github.com/openai/CLIP.git@main#egg=clip
       - -e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
       - -e git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
-      - -e git+https://github.com/invoke-ai/clipseg.git#egg=clipseg
+      - -e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
       - -e .
 variables:
   PYTORCH_ENABLE_MPS_FALLBACK: 1

environment.yml

@@ -37,5 +37,5 @@ dependencies:
     - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
     - -e git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
     - -e git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
-    - -e git+https://github.com/invoke-ai/clipseg.git#egg=clipseg
+    - -e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
     - -e .

frontend/dist/assets/index.89883620.js

@@ -1 +0,0 @@
-

frontend/dist/index.html

@@ -6,7 +6,7 @@
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>InvokeAI - A Stable Diffusion Toolkit</title>
   <link rel="shortcut icon" type="icon" href="/assets/favicon.0d253ced.ico" />
-  <script type="module" crossorigin src="/assets/index.dcc1d08e.js"></script>
+  <script type="module" crossorigin src="/assets/index.b06af007.js"></script>
   <link rel="stylesheet" href="/assets/index.58175ea1.css">
 </head>
 
@@ -15,4 +15,4 @@
   
 </body>
 
-</html>
+</html>

frontend/src/features/gallery/gallerySlice.ts

@@ -72,7 +72,13 @@ export const gallerySlice = createSlice({
     },
     addImage: (state, action: PayloadAction<InvokeAI.Image>) => {
       const newImage = action.payload;
-      const { uuid, mtime } = newImage;
+      const { uuid, url, mtime } = newImage;
+
+      // Do not add duplicate images
+      if (state.images.find((i) => i.url === url && i.mtime === mtime)) {
+        return;
+      }
+
       state.images.unshift(newImage);
       state.currentImageUuid = uuid;
       state.intermediateImage = undefined;
@@ -120,8 +126,15 @@ export const gallerySlice = createSlice({
     ) => {
       const { images, areMoreImagesAvailable } = action.payload;
       if (images.length > 0) {
+        // Filter images that already exist in the gallery
+        const newImages = images.filter(
+          (newImage) =>
+            !state.images.find(
+              (i) => i.url === newImage.url && i.mtime === newImage.mtime
+            )
+        );
         state.images = state.images
-          .concat(images)
+          .concat(newImages)
           .sort((a, b) => b.mtime - a.mtime);
 
         if (!state.currentImage) {

ldm/generate.py

@@ -729,7 +729,7 @@ class Generate:
         
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
-            model.embedding_manager.load(
+            self.model.embedding_manager.load(
                 self.embedding_path, self.precision == 'float32' or self.precision == 'autocast'
             )
 
@@ -806,6 +806,23 @@ class Generate:
             else:
                 r[0] = image
 
+    def apply_textmask(self, image_path:str, prompt:str, callback, threshold:float=0.5):
+        assert os.path.exists(image_path), '** "{image_path}" not found. Please enter the name of an existing image file to mask **'
+        basename,_ = os.path.splitext(os.path.basename(image_path))
+        if self.txt2mask is None:
+            self.txt2mask  = Txt2Mask(device = self.device)
+        segmented  = self.txt2mask.segment(image_path,prompt)
+        trans = segmented.to_transparent()
+        inverse = segmented.to_transparent(invert=True)
+        mask = segmented.to_mask(threshold)
+
+        path_filter = re.compile(r'[<>:"/\\|?*]')
+        safe_prompt = path_filter.sub('_', prompt)[:50].rstrip(' .')
+
+        callback(trans,f'{safe_prompt}.deselected',use_prefix=basename)
+        callback(inverse,f'{safe_prompt}.selected',use_prefix=basename)
+        callback(mask,f'{safe_prompt}.masked',use_prefix=basename)
+
     # to help WebGUI - front end to generator util function
     def sample_to_image(self, samples):
         return self._make_base().sample_to_image(samples)

ldm/invoke/readline.py

@@ -56,6 +56,7 @@ COMMANDS = (
     '--png_compression','-z',
     '--text_mask','-tm',
     '!fix','!fetch','!history','!search','!clear',
+    '!mask',
     '!models','!switch','!import_model','!edit_model'
     )
 MODEL_COMMANDS = (
@@ -71,6 +72,7 @@ IMG_PATH_COMMANDS = (
 IMG_FILE_COMMANDS=(
     '!fix',
     '!fetch',
+    '!mask',
     '--init_img[=\s]','-I',
     '--init_mask[=\s]','-M',
     '--init_color[=\s]',

ldm/invoke/restoration/codeformer.py

@@ -41,10 +41,12 @@ class CodeFormerRestoration():
             cf.eval()
 
             image = image.convert('RGB')
+            # Codeformer expects a BGR np array; make array and flip channels
+            bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
 
             face_helper = FaceRestoreHelper(upscale_factor=1, use_parse=True, device=device)
             face_helper.clean_all()
-            face_helper.read_image(np.array(image, dtype=np.uint8))
+            face_helper.read_image(bgr_image_array)
             face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
             face_helper.align_warp_face()
 
@@ -71,7 +73,8 @@ class CodeFormerRestoration():
 
             restored_img = face_helper.paste_faces_to_input_image()
 
-            res = Image.fromarray(restored_img)
+            # Flip the channels back to RGB
+            res = Image.fromarray(restored_img[...,::-1])
 
             if strength < 1.0:
                 # Resize the image to the new image if the sizes have changed

ldm/invoke/restoration/gfpgan.py

@@ -55,13 +55,18 @@ class GFPGAN():
 
         image = image.convert('RGB')
 
+        # GFPGAN expects a BGR np array; make array and flip channels
+        bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
+
         _, _, restored_img = self.gfpgan.enhance(
-            np.array(image, dtype=np.uint8),
+            bgr_image_array,
             has_aligned=False,
             only_center_face=False,
             paste_back=True,
         )
-        res = Image.fromarray(restored_img)
+
+        # Flip the channels back to RGB
+        res = Image.fromarray(restored_img[...,::-1])
 
         if strength < 1.0:
             # Resize the image to the new image if the sizes have changed

ldm/invoke/restoration/realesrgan.py

@@ -60,14 +60,18 @@ class ESRGAN():
             print(
                 f'>> Real-ESRGAN Upscaling seed:{seed} : scale:{upsampler_scale}x'
             )
+            
+        # REALSRGAN expects a BGR np array; make array and flip channels
+        bgr_image_array = np.array(image, dtype=np.uint8)[...,::-1]
         
         output, _ = upsampler.enhance(
-            np.array(image, dtype=np.uint8),
+            bgr_image_array,
             outscale=upsampler_scale,
             alpha_upsampler='realesrgan',
         )
 
-        res = Image.fromarray(output)
+        # Flip the channels back to RGB
+        res = Image.fromarray(output[...,::-1])
 
         if strength < 1.0:
             # Resize the image to the new image if the sizes have changed

ldm/invoke/txt2mask.py

@@ -29,9 +29,9 @@ work fine.
 
 import torch
 import numpy as  np
-from models.clipseg import CLIPDensePredT
+from clipseg_models.clipseg import CLIPDensePredT
 from einops import rearrange, repeat
-from PIL import Image
+from PIL import Image, ImageOps
 from torchvision import transforms
 
 CLIP_VERSION = 'ViT-B/16'
@@ -50,9 +50,14 @@ class SegmentedGrayscale(object):
         discrete_heatmap = self.heatmap.lt(threshold).int()
         return self._rescale(Image.fromarray(np.uint8(discrete_heatmap*255),mode='L'))
 
-    def to_transparent(self)->Image:
+    def to_transparent(self,invert:bool=False)->Image:
         transparent_image = self.image.copy()
-        transparent_image.putalpha(self.to_grayscale())
+        gs = self.to_grayscale()
+        # The following line looks like a bug, but isn't.
+        # For img2img, we want the selected regions to be transparent,
+        # but to_grayscale() returns the opposite.
+        gs = ImageOps.invert(gs) if not invert else gs
+        transparent_image.putalpha(gs)
         return transparent_image
 
     # unscales and uncrops the 352x352 heatmap so that it matches the image again
@@ -79,7 +84,7 @@ class Txt2Mask(object):
         self.model.load_state_dict(torch.load(CLIPSEG_WEIGHTS, map_location=torch.device('cpu')), strict=False)
 
     @torch.no_grad()
-    def segment(self, image:Image, prompt:str) -> SegmentedGrayscale:
+    def segment(self, image, prompt:str) -> SegmentedGrayscale:
         '''
         Given a prompt string such as "a bagel", tries to identify the object in the
         provided image and returns a SegmentedGrayscale object in which the brighter
@@ -94,6 +99,10 @@ class Txt2Mask(object):
             transforms.Resize((CLIPSEG_SIZE, CLIPSEG_SIZE)), # must be multiple of 64...
         ])
 
+        if type(image) is str:
+            image = Image.open(image).convert('RGB')
+
+        image = ImageOps.exif_transpose(image)
         img = self._scale_and_crop(image)
         img = transform(img).unsqueeze(0)
 

requirements-linux-arm64.txt

@@ -22,5 +22,5 @@ transformers==4.21.3
 -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
 -e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
 -e git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
--3 git+https://github.com/invoke-ai/clipseg.git#egg=clipseg
+-e git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg
 -e .

requirements.txt

@@ -35,4 +35,4 @@ realesrgan
 git+https://github.com/openai/CLIP.git@main#egg=clip
 git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
 git+https://github.com/TencentARC/GFPGAN.git#egg=gfpgan
-git+https://github.com/invoke-ai/clipseg.git#egg=clipseg
+git+https://github.com/invoke-ai/clipseg.git@models-rename#egg=clipseg

scripts/invoke.py

@@ -225,9 +225,13 @@ def main_loop(gen, opt, infile):
                 os.makedirs(opt.outdir)
             current_outdir = opt.outdir
 
-        # write out the history at this point
+        # Write out the history at this point.
+        # TODO: Fix the parsing of command-line parameters
+        # so that !operations don't need to be stripped and readded
         if operation == 'postprocess':
             completer.add_history(f'!fix {command}')
+        elif operation == 'mask':
+            completer.add_history(f'!mask {command}')
         else:
             completer.add_history(command)
 
@@ -247,13 +251,28 @@ def main_loop(gen, opt, infile):
                 # when the -v switch is used to generate variations
                 nonlocal prior_variations
                 nonlocal prefix
-                if use_prefix is not None:
-                    prefix = use_prefix
 
                 path = None
                 if opt.grid:
                     grid_images[seed] = image
+
+                elif operation == 'mask':
+                    filename = f'{prefix}.{use_prefix}.{seed}.png'
+                    tm = opt.text_mask[0]
+                    th = opt.text_mask[1] if len(opt.text_mask)>1 else 0.5
+                    formatted_dream_prompt = f'!mask {opt.prompt} -tm {tm} {th}'
+                    path = file_writer.save_image_and_prompt_to_png(
+                        image           = image,
+                        dream_prompt    = formatted_dream_prompt,
+                        metadata        = {},
+                        name      = filename,
+                        compress_level = opt.png_compression,
+                    )
+                    results.append([path, formatted_dream_prompt])
+
                 else:
+                    if use_prefix is not None:
+                        prefix = use_prefix
                     postprocessed = upscaled if upscaled else operation=='postprocess'
                     filename, formatted_dream_prompt = prepare_image_metadata(
                         opt,
@@ -292,7 +311,7 @@ def main_loop(gen, opt, infile):
                         results.append([path, formatted_dream_prompt])
 
                 # so that the seed autocompletes (on linux|mac when -S or --seed specified
-                if completer:
+                if completer and operation == 'generate':
                     completer.add_seed(seed)
                     completer.add_seed(first_seed)
                 last_results.append([path, seed])
@@ -310,6 +329,10 @@ def main_loop(gen, opt, infile):
                 print(f'>> fixing {opt.prompt}')
                 opt.last_operation = do_postprocess(gen,opt,image_writer)
 
+            elif operation == 'mask':
+                print(f'>> generating masks from {opt.prompt}')
+                do_textmask(gen, opt, image_writer)
+
             if opt.grid and len(grid_images) > 0:
                 grid_img   = make_grid(list(grid_images.values()))
                 grid_seeds = list(grid_images.keys())
@@ -355,6 +378,10 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         command = command.replace('!fix ','',1)
         operation = 'postprocess'
 
+    elif command.startswith('!mask'):
+        command = command.replace('!mask ','',1)
+        operation = 'mask'
+
     elif command.startswith('!switch'):
         model_name = command.replace('!switch ','',1)
         gen.set_model(model_name)
@@ -363,6 +390,7 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         
     elif command.startswith('!models'):
         gen.model_cache.print_models()
+        completer.add_history(command)
         operation = None
 
     elif command.startswith('!import'):
@@ -494,6 +522,19 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False):
     os.rename(tmpfile,conf_path)
     return True
 
+def do_textmask(gen, opt, callback):
+    image_path = opt.prompt
+    assert os.path.exists(image_path), '** "{image_path}" not found. Please enter the name of an existing image file to mask **'
+    assert opt.text_mask is not None and len(opt.text_mask) >= 1, '** Please provide a text mask with -tm **'
+    tm = opt.text_mask[0]
+    threshold = float(opt.text_mask[1]) if len(opt.text_mask) > 1  else 0.5
+    gen.apply_textmask(
+        image_path = image_path,
+        prompt = tm,
+        threshold = threshold,
+        callback = callback,
+    )
+
 def do_postprocess (gen, opt, callback):
     file_path = opt.prompt     # treat the prompt as the file pathname
     if os.path.dirname(file_path) == '': #basename given
@@ -670,7 +711,7 @@ def load_face_restoration(opt):
         print(traceback.format_exc(), file=sys.stderr)
         print('>> You may need to install the ESRGAN and/or GFPGAN modules')
     return gfpgan,codeformer,esrgan
-    
+
 def make_step_callback(gen, opt, prefix):
     destination = os.path.join(opt.outdir,'intermediates',prefix)
     os.makedirs(destination,exist_ok=True)

scripts/preload_models.py

@@ -107,25 +107,27 @@ except Exception:
     print(traceback.format_exc())
 print('...success')
 
-print('Loading clipseq model for text-based masking...',end='')
+print('Loading clipseg model for text-based masking...',end='')
 try:
     model_url  = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
     model_dest = 'src/clipseg/clipseg_weights.zip'
-    if not os.path.exists(model_dest):
+    weights_dir = 'src/clipseg/weights'
+    if not os.path.exists(weights_dir):
         os.makedirs(os.path.dirname(model_dest), exist_ok=True)
         urllib.request.urlretrieve(model_url,model_dest)
         with zipfile.ZipFile(model_dest,'r') as zip:
             zip.extractall('src/clipseg')
             os.rename('src/clipseg/clipseg_weights','src/clipseg/weights')
-        from models.clipseg import CLIPDensePredT
+        os.remove(model_dest)
+        from clipseg_models.clipseg import CLIPDensePredT
         model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, )
         model.eval()
         model.load_state_dict(
-            torch.load('src/clipseg/weights/rd64-uni-refined.pth'),
-            model.load_state_dict(torch.load('src/clipseg/weights/rd64-uni-refined.pth'),
-                                  map_location=torch.device('cpu'),
-                                  strict=False,
-            )
+            torch.load(
+                'src/clipseg/weights/rd64-uni-refined.pth',
+                map_location=torch.device('cpu')
+                ),
+            strict=False,
         )
 except Exception:
     print('Error installing clipseg model:')