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README.md

@@ -11,28 +11,21 @@ pretty_name: Human Segmentation Dataset
 
 This dataset was created **for developing the best fully open-source background remover** of images with humans. It was crafted with [LayerDiffuse](https://github.com/layerdiffusion/LayerDiffuse), a Stable Diffusion extension for generating transparent images. After creating segmented humans, [IC-Light](https://github.com/lllyasviel/IC-Light) was used for embedding them into realistic scenarios.
 
-The dataset covers a diverse set of segmented humans: various skin tones, clothes, hair styles etc. Since Stable Diffusion is not perfect, the dataset contains images with flaws. Still the dataset is good enough for training background remover models.
+The dataset covers a diverse set of segmented humans: various skin tones, clothes, hair styles etc. Since Stable Diffusion is not perfect, the dataset contains images with flaws. Still the dataset is good enough for training background remover models. I created more than 7.000 images with people and diverse backgrounds.
 
-It contains transparent images of humans (`/humans`) which are randomly combined with backgrounds (`/backgrounds`) with an augmentation script.
-
-I created more than 7.000 images with people and diverse backgrounds.
-
-# Create Training Dataset
+# Examples
 
-1. [Download human segmentations and backgrounds](https://drive.google.com/drive/folders/1K1lK6nSoaQ7PLta-bcfol3XSGZA1b9nt?usp=drive_link)
+[LayerDiffuse](https://github.com/layerdiffusion/LayerDiffuse) output:
 
-2. Execute the following script for creating training and validation data:
+![](layer_diffuse_example.png)
 
-```
-./create_dataset.sh
-```
+[IC-Light](https://github.com/lllyasviel/IC-Light) applied to segmented image:
 
-# Examples
+![](final_image_example.png)
 
-Here you can see an augmented image and the resulting ground truth:
+Ground truth:
 
-![](example_image.png)
-![](example_ground_truth.png)
+![](ground_truth_example.png)
 
 # Support
 

util/ic-light.py

@@ -10,27 +10,46 @@ import cv2
 from diffusers.utils import load_image
 
 from PIL import Image, ImageFilter, ImageOps
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionLatentUpscalePipeline
-from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
+from diffusers import (
+    StableDiffusionPipeline,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionLatentUpscalePipeline,
+)
+from diffusers import (
+    AutoencoderKL,
+    UNet2DConditionModel,
+    DDIMScheduler,
+    EulerAncestralDiscreteScheduler,
+    DPMSolverMultistepScheduler,
+)
 from diffusers.models.attention_processor import AttnProcessor2_0
 from transformers import CLIPTextModel, CLIPTokenizer
 from enum import Enum
+
 # from torch.hub import download_url_to_file
 
 
 # 'stablediffusionapi/realistic-vision-v51'
 # 'runwayml/stable-diffusion-v1-5'
-sd15_name = 'stablediffusionapi/realistic-vision-v51'
+sd15_name = "stablediffusionapi/realistic-vision-v51"
 tokenizer = CLIPTokenizer.from_pretrained(sd15_name, subfolder="tokenizer")
 text_encoder = CLIPTextModel.from_pretrained(sd15_name, subfolder="text_encoder")
 vae = AutoencoderKL.from_pretrained(sd15_name, subfolder="vae")
 unet = UNet2DConditionModel.from_pretrained(sd15_name, subfolder="unet")
-upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16)
+upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(
+    "stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16
+)
 
 # Change UNet
 
 with torch.no_grad():
-    new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
+    new_conv_in = torch.nn.Conv2d(
+        8,
+        unet.conv_in.out_channels,
+        unet.conv_in.kernel_size,
+        unet.conv_in.stride,
+        unet.conv_in.padding,
+    )
     new_conv_in.weight.zero_()
     new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
     new_conv_in.bias = unet.conv_in.bias
@@ -40,10 +59,10 @@ unet_original_forward = unet.forward
 
 
 def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
-    c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
+    c_concat = kwargs["cross_attention_kwargs"]["concat_conds"].to(sample)
     c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
     new_sample = torch.cat([sample, c_concat], dim=1)
-    kwargs['cross_attention_kwargs'] = {}
+    kwargs["cross_attention_kwargs"] = {}
     return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
 
 
@@ -51,7 +70,7 @@ unet.forward = hooked_unet_forward
 
 # Load
 
-model_path = './models/iclight_sd15_fc.safetensors'
+model_path = "./models/iclight_sd15_fc.safetensors"
 # download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', dst=model_path)
 sd_offset = sf.load_file(model_path)
 sd_origin = unet.state_dict()
@@ -62,7 +81,7 @@ del sd_offset, sd_origin, sd_merged, keys
 
 # Device
 
-device = torch.device('cuda')
+device = torch.device("cuda")
 text_encoder = text_encoder.to(device=device, dtype=torch.float16)
 vae = vae.to(device=device, dtype=torch.bfloat16)
 unet = unet.to(device=device, dtype=torch.float16)
@@ -85,10 +104,7 @@ ddim_scheduler = DDIMScheduler(
 )
 
 euler_a_scheduler = EulerAncestralDiscreteScheduler(
-    num_train_timesteps=1000,
-    beta_start=0.00085,
-    beta_end=0.012,
-    steps_offset=1
+    num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, steps_offset=1
 )
 
 dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
@@ -97,7 +113,7 @@ dpmpp_2m_sde_karras_scheduler = DPMSolverMultistepScheduler(
     beta_end=0.012,
     algorithm_type="sde-dpmsolver++",
     use_karras_sigmas=True,
-    steps_offset=1
+    steps_offset=1,
 )
 
 # Pipelines
@@ -111,7 +127,7 @@ t2i_pipe = StableDiffusionPipeline(
     safety_checker=None,
     requires_safety_checker=False,
     feature_extractor=None,
-    image_encoder=None
+    image_encoder=None,
 )
 
 i2i_pipe = StableDiffusionImg2ImgPipeline(
@@ -123,7 +139,7 @@ i2i_pipe = StableDiffusionImg2ImgPipeline(
     safety_checker=None,
     requires_safety_checker=False,
     feature_extractor=None,
-    image_encoder=None
+    image_encoder=None,
 )
 
 
@@ -139,7 +155,10 @@ def encode_prompt_inner(txt: str):
         return x[:i] if len(x) >= i else x + [p] * (i - len(x))
 
     tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
-    chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
+    chunks = [
+        [id_start] + tokens[i : i + chunk_length] + [id_end]
+        for i in range(0, len(tokens), chunk_length)
+    ]
     chunks = [pad(ck, id_pad, max_length) for ck in chunks]
 
     token_ids = torch.tensor(chunks).to(device=device, dtype=torch.int64)
@@ -188,7 +207,9 @@ def pytorch2numpy(imgs, quant=True):
 
 @torch.inference_mode()
 def numpy2pytorch(imgs):
-    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0  # so that 127 must be strictly 0.0
+    h = (
+        torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0
+    )  # so that 127 must be strictly 0.0
     h = h.movedim(-1, 1)
     return h
 
@@ -213,14 +234,31 @@ def resize_without_crop(image, target_width, target_height):
     resized_image = pil_image.resize((target_width, target_height), Image.LANCZOS)
     return np.array(resized_image)
 
+
 def remove_alpha_threshold(image, alpha_threshold=160):
     # This function removes artifacts created by LayerDiffusion
     mask = image[:, :, 3] < alpha_threshold
     image[mask] = [0, 0, 0, 0]
     return image
 
+
 @torch.inference_mode()
-def process(input_fg, prompt, image_width, image_height, num_samples, seed, steps, a_prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source):
+def process(
+    input_fg,
+    prompt,
+    image_width,
+    image_height,
+    num_samples,
+    seed,
+    steps,
+    a_prompt,
+    n_prompt,
+    cfg,
+    highres_scale,
+    highres_denoise,
+    lowres_denoise,
+    bg_source,
+):
     bg_source = BGSource(bg_source)
     input_bg = None
 
@@ -243,56 +281,69 @@ def process(input_fg, prompt, image_width, image_height, num_samples, seed, step
         image = np.tile(gradient, (1, image_width))
         input_bg = np.stack((image,) * 3, axis=-1).astype(np.uint8)
     else:
-        raise 'Wrong initial latent!'
+        raise "Wrong initial latent!"
 
     rng = torch.Generator(device=device).manual_seed(int(seed))
 
     fg = resize_and_center_crop(input_fg, image_width, image_height)
 
     concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
-    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    concat_conds = (
+        vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    )
 
-    conds, unconds = encode_prompt_pair(positive_prompt=prompt + ', ' + a_prompt, negative_prompt=n_prompt)
+    conds, unconds = encode_prompt_pair(
+        positive_prompt=prompt + ", " + a_prompt, negative_prompt=n_prompt
+    )
 
     if input_bg is None:
-        latents = t2i_pipe(
-            prompt_embeds=conds,
-            negative_prompt_embeds=unconds,
-            width=image_width,
-            height=image_height,
-            num_inference_steps=steps,
-            num_images_per_prompt=num_samples,
-            generator=rng,
-            output_type='latent',
-            guidance_scale=cfg,
-            cross_attention_kwargs={'concat_conds': concat_conds},
-        ).images.to(vae.dtype) / vae.config.scaling_factor
+        latents = (
+            t2i_pipe(
+                prompt_embeds=conds,
+                negative_prompt_embeds=unconds,
+                width=image_width,
+                height=image_height,
+                num_inference_steps=steps,
+                num_images_per_prompt=num_samples,
+                generator=rng,
+                output_type="latent",
+                guidance_scale=cfg,
+                cross_attention_kwargs={"concat_conds": concat_conds},
+            ).images.to(vae.dtype)
+            / vae.config.scaling_factor
+        )
     else:
         bg = resize_and_center_crop(input_bg, image_width, image_height)
         bg_latent = numpy2pytorch([bg]).to(device=vae.device, dtype=vae.dtype)
         bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor
-        latents = i2i_pipe(
-            image=bg_latent,
-            strength=lowres_denoise,
-            prompt_embeds=conds,
-            negative_prompt_embeds=unconds,
-            width=image_width,
-            height=image_height,
-            num_inference_steps=int(round(steps / lowres_denoise)),
-            num_images_per_prompt=num_samples,
-            generator=rng,
-            output_type='latent',
-            guidance_scale=cfg,
-            cross_attention_kwargs={'concat_conds': concat_conds},
-        ).images.to(vae.dtype) / vae.config.scaling_factor
+        latents = (
+            i2i_pipe(
+                image=bg_latent,
+                strength=lowres_denoise,
+                prompt_embeds=conds,
+                negative_prompt_embeds=unconds,
+                width=image_width,
+                height=image_height,
+                num_inference_steps=int(round(steps / lowres_denoise)),
+                num_images_per_prompt=num_samples,
+                generator=rng,
+                output_type="latent",
+                guidance_scale=cfg,
+                cross_attention_kwargs={"concat_conds": concat_conds},
+            ).images.to(vae.dtype)
+            / vae.config.scaling_factor
+        )
 
     pixels = vae.decode(latents).sample
     pixels = pytorch2numpy(pixels)
-    pixels = [resize_without_crop(
-        image=p,
-        target_width=int(round(image_width * highres_scale / 64.0) * 64),
-        target_height=int(round(image_height * highres_scale / 64.0) * 64))
-    for p in pixels]
+    pixels = [
+        resize_without_crop(
+            image=p,
+            target_width=int(round(image_width * highres_scale / 64.0) * 64),
+            target_height=int(round(image_height * highres_scale / 64.0) * 64),
+        )
+        for p in pixels
+    ]
 
     pixels = numpy2pytorch(pixels).to(device=vae.device, dtype=vae.dtype)
     latents = vae.encode(pixels).latent_dist.mode() * vae.config.scaling_factor
@@ -302,22 +353,27 @@ def process(input_fg, prompt, image_width, image_height, num_samples, seed, step
 
     fg = resize_and_center_crop(input_fg, image_width, image_height)
     concat_conds = numpy2pytorch([fg]).to(device=vae.device, dtype=vae.dtype)
-    concat_conds = vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
-
-    latents = i2i_pipe(
-        image=latents,
-        strength=highres_denoise,
-        prompt_embeds=conds,
-        negative_prompt_embeds=unconds,
-        width=image_width,
-        height=image_height,
-        num_inference_steps=int(round(steps / highres_denoise)),
-        num_images_per_prompt=num_samples,
-        generator=rng,
-        output_type='latent',
-        guidance_scale=cfg,
-        cross_attention_kwargs={'concat_conds': concat_conds},
-    ).images.to(vae.dtype) / vae.config.scaling_factor
+    concat_conds = (
+        vae.encode(concat_conds).latent_dist.mode() * vae.config.scaling_factor
+    )
+
+    latents = (
+        i2i_pipe(
+            image=latents,
+            strength=highres_denoise,
+            prompt_embeds=conds,
+            negative_prompt_embeds=unconds,
+            width=image_width,
+            height=image_height,
+            num_inference_steps=int(round(steps / highres_denoise)),
+            num_images_per_prompt=num_samples,
+            generator=rng,
+            output_type="latent",
+            guidance_scale=cfg,
+            cross_attention_kwargs={"concat_conds": concat_conds},
+        ).images.to(vae.dtype)
+        / vae.config.scaling_factor
+    )
 
     pixels = vae.decode(latents).sample
 
@@ -335,16 +391,18 @@ def augment(image):
     else:
         target_height, target_width = 512 * 2, 640 * 2
 
-    left_right_padding = (max(target_width, image_width) - min(target_width, image_width)) // 2
+    left_right_padding = (
+        max(target_width, image_width) - min(target_width, image_width)
+    ) // 2
 
     original = cv2.copyMakeBorder(
-        original, 
-        top=max(target_height, image_height) - min(target_height, image_height), 
+        original,
+        top=max(target_height, image_height) - min(target_height, image_height),
         bottom=0,
-        left=left_right_padding, 
-        right=left_right_padding, 
-        borderType=cv2.BORDER_CONSTANT, 
-        value=(0, 0, 0)
+        left=left_right_padding,
+        right=left_right_padding,
+        borderType=cv2.BORDER_CONSTANT,
+        value=(0, 0, 0),
     )
 
     transform = A.Compose(
@@ -363,6 +421,7 @@ def augment(image):
 
     return transform(image=original)["image"]
 
+
 class BGSource(Enum):
     NONE = "None"
     LEFT = "Left Light"
@@ -380,8 +439,7 @@ prompts = [
     "sunshine, cafe, chilled",
     "exhibition, paintings",
     "beach",
-    "winter, snow"
-    "forrest, cloudy",
+    "winter, snow" "forrest, cloudy",
     "party, people",
     "cozy living room, sofa, shelf",
     "mountains",
@@ -392,7 +450,7 @@ prompts = [
     "appartment, soft light",
     "garden",
     "school",
-    "art exhibition with paintings in background"
+    "art exhibition with paintings in background",
 ]
 
 os.makedirs(ground_truth_dir, exist_ok=True)
@@ -402,7 +460,9 @@ all_images = os.listdir(input_dir)
 random.shuffle(all_images)
 
 for filename in all_images:
-    if filename.lower().endswith(('.png', '.jpg', '.jpeg')):  # Check if the file is an image
+    if filename.lower().endswith(
+        (".png", ".jpg", ".jpeg")
+    ):  # Check if the file is an image
 
         letters = string.ascii_lowercase
         random_string = "".join(random.choice(letters) for i in range(13))
@@ -418,7 +478,9 @@ for filename in all_images:
         image = np.array(image)
 
         image_augmented = augment(image)
-        Image.fromarray(image_augmented).getchannel("A").save(os.path.join(ground_truth_dir, random_filename))
+        Image.fromarray(image_augmented).getchannel("A").save(
+            os.path.join(ground_truth_dir, random_filename)
+        )
 
         image_augmented = image_augmented[:, :, :3]
 
@@ -427,7 +489,7 @@ for filename in all_images:
         image_height, image_width, _ = image_augmented.shape
 
         num_samples = 1
-        seed = random.randint(1,123456789012345678901234567890)
+        seed = random.randint(1, 123456789012345678901234567890)
         steps = 25
         constant_prompt = "details, high quality"
         prompt = random.choice(prompts)
@@ -437,7 +499,22 @@ for filename in all_images:
         highres_denoise = 0.7
         lowres_denoise = 0.5
         bg_source = BGSource.NONE
-  
-        results = process(image_augmented, constant_prompt, image_width, image_height, num_samples, seed, steps, prompt, n_prompt, cfg, highres_scale, highres_denoise, lowres_denoise, bg_source)
+
+        results = process(
+            image_augmented,
+            constant_prompt,
+            image_width,
+            image_height,
+            num_samples,
+            seed,
+            steps,
+            prompt,
+            n_prompt,
+            cfg,
+            highres_scale,
+            highres_denoise,
+            lowres_denoise,
+            bg_source,
+        )
         result_image = Image.fromarray(results[0])
-        result_image.save(os.path.join(image_dir, random_filename))‚
+        result_image.save(os.path.join(image_dir, random_filename))