Merge branch 'inpaint-improvement' of https://github.com/Kyle0654/InvokeAI into inpaint-improvement

configs/models.yaml

@@ -1,21 +1,21 @@
 # This file describes the alternative machine learning models
-#  available to the dream script.
+# available to the dream script.
 #
 # To add a new model, follow the examples below. Each
 # model requires a model config file, a weights file,
 # and the width and height of the images it
 # was trained on.
-
 stable-diffusion-1.4:
-    config:  configs/stable-diffusion/v1-inference.yaml
+  config:  configs/stable-diffusion/v1-inference.yaml
-    weights: models/ldm/stable-diffusion-v1/model.ckpt
+  weights: models/ldm/stable-diffusion-v1/model.ckpt
-    description: Stable Diffusion inference model version 1.4
+  description: Stable Diffusion inference model version 1.4
-    width: 512
+  width: 512
-    height: 512
+  height: 512
-    default: true
+  default: true
 stable-diffusion-1.5:
-    config:  configs/stable-diffusion/v1-inference.yaml
+  config: configs/stable-diffusion/v1-inference.yaml
-    weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+  weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
-    description: Stable Diffusion inference model version 1.5
+  description: Stable Diffusion inference model version 1.5
-    width: 512
+  width: 512
-    height: 512
+  height: 512
+  vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt

ldm/generate.py

@@ -58,24 +58,6 @@ torch.multinomial = fix_func(torch.multinomial)
 # this is fallback model in case no default is defined
 FALLBACK_MODEL_NAME='stable-diffusion-1.4'
 
-def fix_func(orig):
-    if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
-        def new_func(*args, **kw):
-            device = kw.get("device", "mps")
-            kw["device"]="cpu"
-            return orig(*args, **kw).to(device)
-        return new_func
-    return orig
-
-torch.rand = fix_func(torch.rand)
-torch.rand_like = fix_func(torch.rand_like)
-torch.randn = fix_func(torch.randn)
-torch.randn_like = fix_func(torch.randn_like)
-torch.randint = fix_func(torch.randint)
-torch.randint_like = fix_func(torch.randint_like)
-torch.bernoulli = fix_func(torch.bernoulli)
-torch.multinomial = fix_func(torch.multinomial)
-
 """Simplified text to image API for stable diffusion/latent diffusion
 
 Example Usage:
@@ -411,7 +393,7 @@ class Generate:
                 log_tokens    =self.log_tokenization
             )
 
-            init_image,mask_image,pil_image,pil_mask = self._make_images(
+            init_image, mask_image = self._make_images(
                 init_img,
                 init_mask,
                 width,
@@ -451,8 +433,6 @@ class Generate:
                 height=height,
                 init_img=init_img,        # embiggen needs to manipulate from the unmodified init_img
                 init_image=init_image,      # notice that init_image is different from init_img
-                pil_image=pil_image,
-                pil_mask=pil_mask,
                 mask_image=mask_image,
                 strength=strength,
                 threshold=threshold,
@@ -644,7 +624,7 @@ class Generate:
         init_image      = None
         init_mask       = None
         if not img:
-            return None, None, None, None
+            return None, None
 
         image = self._load_img(img)
 
@@ -654,23 +634,22 @@ class Generate:
         # if image has a transparent area and no mask was provided, then try to generate mask
         if self._has_transparency(image):
             self._transparency_check_and_warning(image, mask)
-            # this returns a torch tensor
             init_mask = self._create_init_mask(image, width, height, fit=fit)
             
         if (image.width * image.height) > (self.width * self.height) and self.size_matters:
             print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
             self.size_matters = False
 
-        init_image   = self._create_init_image(image,width,height,fit=fit)                   # this returns a torch tensor
+        init_image   = self._create_init_image(image,width,height,fit=fit)
 
         if mask:
-            mask_image = self._load_img(mask)  # this returns an Image
+            mask_image = self._load_img(mask)
             init_mask = self._create_init_mask(mask_image,width,height,fit=fit)
 
         elif text_mask:
             init_mask = self._txt2mask(image, text_mask, width, height, fit=fit)
 
-        return init_image, init_mask, image, mask_image
+        return init_image,init_mask
 
     def _make_base(self):
         if not self.generators.get('base'):
@@ -887,33 +866,15 @@ class Generate:
 
     def _create_init_image(self, image, width, height, fit=True):
         image = image.convert('RGB')
-        if fit:
+        image = self._fit_image(image, (width, height)) if fit else self._squeeze_image(image)
-            image = self._fit_image(image, (width, height))
+        return image
-        else:
-            image = self._squeeze_image(image)
-        image = np.array(image).astype(np.float32) / 255.0
-        image = image[None].transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image)
-        image = 2.0 * image - 1.0
-        return image.to(self.device)
 
     def _create_init_mask(self, image, width, height, fit=True):
         # convert into a black/white mask
         image = self._image_to_mask(image)
         image = image.convert('RGB')
-
+        image = self._fit_image(image, (width, height)) if fit else self._squeeze_image(image)
-        # now we adjust the size
+        return image
-        if fit:
-            image = self._fit_image(image, (width, height))
-        else:
-            image = self._squeeze_image(image)
-        image = image.resize((image.width//downsampling, image.height //
-                              downsampling), resample=Image.Resampling.NEAREST)
-        image = np.array(image)
-        image = image.astype(np.float32) / 255.0
-        image = image[None].transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image)
-        return image.to(self.device)
 
     # The mask is expected to have the region to be inpainted
     # with alpha transparency. It converts it into a black/white
@@ -930,7 +891,6 @@ class Generate:
             mask = ImageOps.invert(mask)
         return mask
 
-    # TODO: The latter part of this method repeats code from _create_init_mask()
     def _txt2mask(self, image:Image, text_mask:list, width, height, fit=True) -> Image:
         prompt = text_mask[0]
         confidence_level = text_mask[1] if len(text_mask)>1 else 0.5
@@ -940,18 +900,8 @@ class Generate:
         segmented = self.txt2mask.segment(image, prompt)
         mask = segmented.to_mask(float(confidence_level))
         mask = mask.convert('RGB')
-        # now we adjust the size
+        mask = self._fit_image(mask, (width, height)) if fit else self._squeeze_image(mask)
-        if fit:
+        return mask
-            mask = self._fit_image(mask, (width, height))
-        else:
-            mask = self._squeeze_image(mask)
-        mask = mask.resize((mask.width//downsampling, mask.height //
-                              downsampling), resample=Image.Resampling.NEAREST)
-        mask = np.array(mask)
-        mask = mask.astype(np.float32) / 255.0
-        mask = mask[None].transpose(0, 3, 1, 2)
-        mask = torch.from_numpy(mask)
-        return mask.to(self.device)
 
     def _has_transparency(self, image):
         if image.info.get("transparency", None) is not None:

ldm/invoke/generator/img2img.py

@@ -4,9 +4,12 @@ ldm.invoke.generator.img2img descends from ldm.invoke.generator
 
 import torch
 import numpy as  np
-from ldm.invoke.devices             import choose_autocast
+import PIL
-from ldm.invoke.generator.base      import Generator
+from torch import Tensor
-from ldm.models.diffusion.ddim     import DDIMSampler
+from PIL import Image
+from ldm.invoke.devices import choose_autocast
+from ldm.invoke.generator.base import Generator
+from ldm.models.diffusion.ddim import DDIMSampler
 
 class Img2Img(Generator):
     def __init__(self, model, precision):
@@ -25,6 +28,9 @@ class Img2Img(Generator):
             ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
         )
 
+        if isinstance(init_image, PIL.Image.Image):
+            init_image = self._image_to_tensor(init_image)
+
         scope = choose_autocast(self.precision)
         with scope(self.model.device.type):
             self.init_latent = self.model.get_first_stage_encoding(
@@ -68,3 +74,11 @@ class Img2Img(Generator):
             shape = init_latent.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
         return x
+
+    def _image_to_tensor(self, image:Image, normalize:bool=True)->Tensor:
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image[None].transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image)
+        if normalize:
+            image = 2.0 * image - 1.0
+        return image.to(self.model.device)    

ldm/invoke/generator/inpaint.py

@@ -6,6 +6,7 @@ import torch
 import torchvision.transforms as T
 import numpy as  np
 import cv2 as cv
+import PIL
 from PIL import Image, ImageFilter
 from skimage.exposure.histogram_matching import match_histograms
 from einops import rearrange, repeat
@@ -13,16 +14,19 @@ from ldm.invoke.devices             import choose_autocast
 from ldm.invoke.generator.img2img   import Img2Img
 from ldm.models.diffusion.ddim     import DDIMSampler
 from ldm.models.diffusion.ksampler import KSampler
+from ldm.invoke.generator.base import downsampling
 
 class Inpaint(Img2Img):
     def __init__(self, model, precision):
         self.init_latent = None
+        self.pil_image = None
+        self.pil_mask = None
+        self.mask_blur_radius = 0
         super().__init__(model, precision)
 
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                        conditioning,init_image,mask_image,strength,
-                       pil_image: Image.Image, pil_mask: Image.Image,
                        mask_blur_radius: int = 8,
                        step_callback=None,inpaint_replace=False, **kwargs):
         """
@@ -31,17 +35,22 @@ class Inpaint(Img2Img):
         the time you call it.  kwargs are 'init_latent' and 'strength'
         """
 
-        # Get the alpha channel of the mask
+        if isinstance(init_image, PIL.Image.Image):
-        pil_init_mask = pil_mask.getchannel('A')
+            self.pil_image = init_image
-        pil_init_image = pil_image.convert('RGBA') # Add an alpha channel if one doesn't exist
+            init_image = self._image_to_tensor(init_image)
 
-        # Build an image with only visible pixels from source to use as reference for color-matching.
+        if isinstance(mask_image, PIL.Image.Image):
-        # Note that this doesn't use the mask, which would exclude some source image pixels from the
+            self.pil_mask = mask_image
-        # histogram and cause slight color changes.
+            mask_image = mask_image.resize(
-        init_rgb_pixels = np.asarray(pil_image.convert('RGB'), dtype=np.uint8).reshape(pil_image.width * pil_image.height, 3)
+                (
-        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8).reshape(pil_init_mask.width * pil_init_mask.height)
+                    mask_image.width // downsampling,
-        init_rgb_pixels = init_rgb_pixels[init_a_pixels > 0]
+                    mask_image.height // downsampling
-        init_rgb_pixels = init_rgb_pixels.reshape(1, init_rgb_pixels.shape[0], init_rgb_pixels.shape[1]) # Filter to just pixels that have any alpha, this is now our histogram
+                ),
+                resample=Image.Resampling.NEAREST
+            )
+            mask_image = self._image_to_tensor(mask_image,normalize=False)
+
+        self.mask_blur_radius = mask_blur_radius
 
         # klms samplers not supported yet, so ignore previous sampler
         if isinstance(sampler,KSampler):
@@ -96,30 +105,50 @@ class Inpaint(Img2Img):
                 mask                       = mask_image,
                 init_latent                = self.init_latent
             )
-
+            return self.sample_to_image(samples)
-            # Get PIL result
-            gen_result = self.sample_to_image(samples).convert('RGB')
-
-            # Get numpy version
-            np_gen_result = np.asarray(gen_result, dtype=np.uint8)
-
-            # Color correct
-            np_matched_result = match_histograms(np_gen_result, init_rgb_pixels, channel_axis=-1)
-            matched_result = Image.fromarray(np_matched_result, mode='RGB')
-
-
-            # Blur the mask out (into init image) by specified amount
-            if mask_blur_radius > 0:
-                nm = np.asarray(pil_init_mask, dtype=np.uint8)
-                nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
-                pmd = Image.fromarray(nmd, mode='L')
-                blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
-            else:
-                blurred_init_mask = pil_init_mask
-
-            # Paste original on color-corrected generation (using blurred mask)
-            matched_result.paste(pil_image, (0,0), mask = blurred_init_mask)
-
-            return matched_result
 
         return make_image
+
+    def sample_to_image(self, samples)->Image:
+        gen_result = super().sample_to_image(samples).convert('RGB')
+
+        if self.pil_image is None or self.pil_mask is None:
+            return gen_result
+        
+        pil_mask = self.pil_mask
+        pil_image = self.pil_image
+        mask_blur_radius = self.mask_blur_radius
+
+        # Get the original alpha channel of the mask if there is one.
+        # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
+        pil_init_mask = pil_mask.getchannel('A') if pil_mask.mode == 'RGBA' else pil_mask.convert('L')
+        pil_init_image = pil_image.convert('RGBA') # Add an alpha channel if one doesn't exist
+
+        # Build an image with only visible pixels from source to use as reference for color-matching.
+        # Note that this doesn't use the mask, which would exclude some source image pixels from the
+        # histogram and cause slight color changes.
+        init_rgb_pixels = np.asarray(pil_image.convert('RGB'), dtype=np.uint8).reshape(pil_image.width * pil_image.height, 3)
+        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8).reshape(pil_init_mask.width * pil_init_mask.height)
+        init_rgb_pixels = init_rgb_pixels[init_a_pixels > 0]
+        init_rgb_pixels = init_rgb_pixels.reshape(1, init_rgb_pixels.shape[0], init_rgb_pixels.shape[1]) # Filter to just pixels that have any alpha, this is now our histogram
+
+        # Get numpy version
+        np_gen_result = np.asarray(gen_result, dtype=np.uint8)
+
+        # Color correct
+        np_matched_result = match_histograms(np_gen_result, init_rgb_pixels, channel_axis=-1)
+        matched_result = Image.fromarray(np_matched_result, mode='RGB')
+
+        # Blur the mask out (into init image) by specified amount
+        if mask_blur_radius > 0:
+            nm = np.asarray(pil_init_mask, dtype=np.uint8)
+            nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
+            pmd = Image.fromarray(nmd, mode='L')
+            blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
+        else:
+            blurred_init_mask = pil_init_mask
+
+        # Paste original on color-corrected generation (using blurred mask)
+        matched_result.paste(pil_image, (0,0), mask = blurred_init_mask)
+        return matched_result
+

ldm/invoke/model_cache.py

@@ -13,6 +13,7 @@ import gc
 import hashlib
 import psutil
 import transformers
+import os
 from sys import getrefcount
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
@@ -193,6 +194,7 @@ class ModelCache(object):
         mconfig = self.config[model_name]
         config = mconfig.config
         weights = mconfig.weights
+        vae = mconfig.get('vae',None)
         width = mconfig.width
         height = mconfig.height
 
@@ -222,9 +224,17 @@ class ModelCache(object):
         else:
             print('   | Using more accurate float32 precision')
 
+        # look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
+        if vae and os.path.exists(vae):
+            print(f'   | Loading VAE weights from: {vae}')
+            vae_ckpt = torch.load(vae, map_location="cpu")
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            model.first_stage_model.load_state_dict(vae_dict, strict=False)
+
         model.to(self.device)
         # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
         model.cond_stage_model.device = self.device
+        
         model.eval()
 
         for m in model.modules():

scripts/invoke.py

@@ -493,6 +493,16 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
         new_config['config'] = input('Configuration file for this model: ')
         done = os.path.exists(new_config['config'])
 
+    done = False
+    completer.complete_extensions(('.vae.pt','.vae','.ckpt'))
+    while not done:
+        vae = input('VAE autoencoder file for this model [None]: ')
+        if os.path.exists(vae):
+            new_config['vae'] = vae
+            done = True
+        else:
+            done = len(vae)==0
+
     completer.complete_extensions(None)
 
     for field in ('width','height'):
@@ -537,8 +547,8 @@ def edit_config(model_name:str, gen, opt, completer):
 
     conf = config[model_name]
     new_config = {}
-    completer.complete_extensions(('.yaml','.yml','.ckpt','.vae'))
+    completer.complete_extensions(('.yaml','.yml','.ckpt','.vae.pt'))
-    for field in ('description', 'weights', 'config', 'width','height'):
+    for field in ('description', 'weights', 'vae', 'config', 'width','height'):
         completer.linebuffer = str(conf[field]) if field in conf else ''
         new_value = input(f'{field}: ')
         new_config[field] = int(new_value) if field in ('width','height') else new_value