Merge branch 'AUTOMATIC1111:master' into master

modules/textual_inversion/image_embedding.py

@@ -0,0 +1,219 @@
+import base64
+import json
+import numpy as np
+import zlib
+from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
+from fonts.ttf import Roboto
+import torch
+
+
+class EmbeddingEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, torch.Tensor):
+            return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
+        return json.JSONEncoder.default(self, obj)
+
+
+class EmbeddingDecoder(json.JSONDecoder):
+    def __init__(self, *args, **kwargs):
+        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
+
+    def object_hook(self, d):
+        if 'TORCHTENSOR' in d:
+            return torch.from_numpy(np.array(d['TORCHTENSOR']))
+        return d
+
+
+def embedding_to_b64(data):
+    d = json.dumps(data, cls=EmbeddingEncoder)
+    return base64.b64encode(d.encode())
+
+
+def embedding_from_b64(data):
+    d = base64.b64decode(data)
+    return json.loads(d, cls=EmbeddingDecoder)
+
+
+def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
+    while True:
+        seed = (a * seed + c) % m
+        yield seed % 255
+
+
+def xor_block(block):
+    g = lcg()
+    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
+    return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
+
+
+def style_block(block, sequence):
+    im = Image.new('RGB', (block.shape[1], block.shape[0]))
+    draw = ImageDraw.Draw(im)
+    i = 0
+    for x in range(-6, im.size[0], 8):
+        for yi, y in enumerate(range(-6, im.size[1], 8)):
+            offset = 0
+            if yi % 2 == 0:
+                offset = 4
+            shade = sequence[i % len(sequence)]
+            i += 1
+            draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
+
+    fg = np.array(im).astype(np.uint8) & 0xF0
+
+    return block ^ fg
+
+
+def insert_image_data_embed(image, data):
+    d = 3
+    data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
+    data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
+    data_np_high = data_np_ >> 4
+    data_np_low = data_np_ & 0x0F
+
+    h = image.size[1]
+    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
+    next_size = next_size + ((h*d)-(next_size % (h*d)))
+
+    data_np_low.resize(next_size)
+    data_np_low = data_np_low.reshape((h, -1, d))
+
+    data_np_high.resize(next_size)
+    data_np_high = data_np_high.reshape((h, -1, d))
+
+    edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
+    edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
+
+    data_np_low = style_block(data_np_low, sequence=edge_style)
+    data_np_low = xor_block(data_np_low)
+    data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
+    data_np_high = xor_block(data_np_high)
+
+    im_low = Image.fromarray(data_np_low, mode='RGB')
+    im_high = Image.fromarray(data_np_high, mode='RGB')
+
+    background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
+    background.paste(im_low, (0, 0))
+    background.paste(image, (im_low.size[0]+1, 0))
+    background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
+
+    return background
+
+
+def crop_black(img, tol=0):
+    mask = (img > tol).all(2)
+    mask0, mask1 = mask.any(0), mask.any(1)
+    col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
+    row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
+    return img[row_start:row_end, col_start:col_end]
+
+
+def extract_image_data_embed(image):
+    d = 3
+    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
+    black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
+    if black_cols[0].shape[0] < 2:
+        print('No Image data blocks found.')
+        return None
+
+    data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
+    data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
+
+    data_block_lower = xor_block(data_block_lower)
+    data_block_upper = xor_block(data_block_upper)
+
+    data_block = (data_block_upper << 4) | (data_block_lower)
+    data_block = data_block.flatten().tobytes()
+
+    data = zlib.decompress(data_block)
+    return json.loads(data, cls=EmbeddingDecoder)
+
+
+def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
+    from math import cos
+
+    image = srcimage.copy()
+
+    if textfont is None:
+        try:
+            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
+            textfont = opts.font or Roboto
+        except Exception:
+            textfont = Roboto
+
+    factor = 1.5
+    gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
+    for y in range(image.size[1]):
+        mag = 1-cos(y/image.size[1]*factor)
+        mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
+        gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
+    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
+
+    draw = ImageDraw.Draw(image)
+    fontsize = 32
+    font = ImageFont.truetype(textfont, fontsize)
+    padding = 10
+
+    _, _, w, h = draw.textbbox((0, 0), title, font=font)
+    fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
+    font = ImageFont.truetype(textfont, fontsize)
+    _, _, w, h = draw.textbbox((0, 0), title, font=font)
+    draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
+
+    _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
+    fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
+    fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+    _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
+    fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
+
+    font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
+
+    draw.text((padding, image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
+    draw.text((image.size[0]/2, image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
+    draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
+
+    return image
+
+
+if __name__ == '__main__':
+
+    testEmbed = Image.open('test_embedding.png')
+    data = extract_image_data_embed(testEmbed)
+    assert data is not None
+
+    data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
+    assert data is not None
+
+    image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
+    cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
+
+    test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
+
+    embedded_image = insert_image_data_embed(cap_image, test_embed)
+
+    retrived_embed = extract_image_data_embed(embedded_image)
+
+    assert str(retrived_embed) == str(test_embed)
+
+    embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
+
+    assert embedded_image == embedded_image2
+
+    g = lcg()
+    shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
+
+    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177,
+                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179,
+                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193,
+                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28,
+                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0,
+                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185,
+                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82,
+                         204, 86,  73, 222,  44, 198, 118, 240,  97]
+
+    assert shared_random == reference_random
+
+    hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
+
+    assert 12731374 == hunna_kay_random_sum

modules/textual_inversion/textual_inversion.py

@@ -7,11 +7,15 @@ import tqdm
 import html
 import datetime
 
+from PIL import Image, PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnSchedule
 
+from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
+                                                       insert_image_data_embed, extract_image_data_embed,
+                                                       caption_image_overlay)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
@@ -81,7 +85,18 @@ class EmbeddingDatabase:
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
 
-            data = torch.load(path, map_location="cpu")
+            data = []
+
+            if filename.upper().endswith('.PNG'):
+                embed_image = Image.open(path)
+                if 'sd-ti-embedding' in embed_image.text:
+                    data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
+                    name = data.get('name', name)
+                else:
+                    data = extract_image_data_embed(embed_image)
+                    name = data.get('name', name)
+            else:
+                data = torch.load(path, map_location="cpu")
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -157,7 +172,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt):
+
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -179,6 +195,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     else:
         images_dir = None
 
+    if create_image_every > 0 and save_image_with_stored_embedding:
+        images_embeds_dir = os.path.join(log_directory, "image_embeddings")
+        os.makedirs(images_embeds_dir, exist_ok=True)
+    else:
+        images_embeds_dir = None
+        
     cond_model = shared.sd_model.cond_stage_model
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
@@ -262,6 +284,26 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             image = processed.images[0]
 
             shared.state.current_image = image
+
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+
+                info = PngImagePlugin.PngInfo()
+                data = torch.load(last_saved_file)
+                info.add_text("sd-ti-embedding", embedding_to_b64(data))
+
+                title = "<{}>".format(data.get('name', '???'))
+                checkpoint = sd_models.select_checkpoint()
+                footer_left = checkpoint.model_name
+                footer_mid = '[{}]'.format(checkpoint.hash)
+                footer_right = '{}'.format(embedding.step)
+
+                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
+                captioned_image = insert_image_data_embed(captioned_image, data)
+
+                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+
             image.save(last_saved_image)
 
             last_saved_image += f", prompt: {preview_text}"

modules/ui.py

@@ -1109,6 +1109,7 @@ def create_ui(wrap_gradio_gpu_call):
                     num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
+                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
                     preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
 
                     with gr.Row():
@@ -1187,6 +1188,7 @@ def create_ui(wrap_gradio_gpu_call):
                 create_image_every,
                 save_embedding_every,
                 template_file,
+                save_image_with_stored_embedding,
                 preview_image_prompt,
             ],
             outputs=[