cofig files

.github/workflows/test.yml

@@ -0,0 +1,25 @@
+name: Run Python Tests
+
+on:
+  push:
+    branches:
+      - main
+      - master
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Install dependencies
+        run: pip install -r requirements.txt # Adjust this based on your project structure
+
+      - name: Run tests
+        run: python -m unittest discover tests # Adjust this based on your test discovery method

.gitignore

@@ -0,0 +1,34 @@
+# Python
+*.pyc
+__pycache__/
+
+# Virtual environments
+venv/
+env/
+*.env/
+
+# Jupyter Notebook
+.ipynb_checkpoints/
+
+# Model artifacts and data files
+*.h5
+*.pkl
+*.npy
+*.npz
+*.csv
+
+# Logs
+*.log
+
+# Ignore data directory if you store large datasets
+data/
+
+# Ignore model outputs
+models/
+
+# Ignore environment configuration files
+.env
+
+# Ignore system files
+.DS_Store
+Thumbs.db

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+author or copyright holder as a result of your choosing to follow a
+later version.
+
+15. Disclaimer of Warranty.
+
+THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+16. Limitation of Liability.
+
+IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+17. Interpretation of Sections 15 and 16.
+
+If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

notebooks/{sam_1.ipynb → sam/sam_1.ipynb}

@@ -5,7 +5,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# notebook using sam model"
+   ]
   }
  ],
  "metadata": {

prototype/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

prototype/README.md

@@ -0,0 +1,13 @@
+---
+title: Pearl Prototype
+emoji: 💻
+colorFrom: blue
+colorTo: red
+sdk: streamlit
+sdk_version: 1.29.0
+app_file: app.py
+pinned: false
+license: openrail
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

prototype/app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+from io import BytesIO
+
+from torch import autocast
+import requests
+import PIL
+import torch
+from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    revision="fp16",
+    torch_dtype=torch.float16,
+    use_auth_token=True,
+)
+
+
+def process_image(dict, prompt):
+    init_img = dict["image"].convert("RGB").resize((512, 512))
+    mask_img = dict["mask"].convert("RGB").resize((512, 512))
+    images = pipe(
+        prompt=prompt, init_image=init_img, mask_image=mask_img, strength=0.75
+    )["sample"]
+    return images[0]
+
+
+iface = gr.Interface(
+    fn=process_image,
+    title="Stable Diffusion In-Painting Tool on Colab with Gradio",
+    inputs=[
+        gr.Image(source="upload", tool="sketch", type="pil"),
+        gr.Textbox(label="prompt"),
+    ],
+    outputs=[gr.Image()],
+    description="Choose a feature and upload an image to see the processed result.",
+    article="<p style='text-align: center;'>Built with Gradio</p>",
+)
+
+
+iface.launch()

prototype/inpainting.py

@@ -0,0 +1,227 @@
+# credit : Hugging Face Team
+import inspect
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+
+import PIL
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DiffusionPipeline,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from tqdm.auto import tqdm
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+def preprocess_image(image):
+    w, h = image.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
+    image = np.array(image).astype(np.float32) / 255.0
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image)
+    return 2.0 * image - 1.0
+
+
+def preprocess_mask(mask):
+    mask = mask.convert("L")
+    w, h = mask.size
+    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    mask = mask.resize((w // 8, h // 8), resample=PIL.Image.NEAREST)
+    mask = np.array(mask).astype(np.float32) / 255.0
+    mask = np.tile(mask, (4, 1, 1))
+    mask = mask[None].transpose(0, 1, 2, 3)  # what does this step do?
+    mask = 1 - mask  # repaint white, keep black
+    mask = torch.from_numpy(mask)
+    return mask
+
+
+class StableDiffusionInpaintingPipeline(DiffusionPipeline):
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+        scheduler = scheduler.set_format("pt")
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        init_image: torch.FloatTensor,
+        mask_image: torch.FloatTensor,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        eta: Optional[float] = 0.0,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        elif isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+
+        if strength < 0 or strength > 1:
+            raise ValueError(
+                f"The value of strength should in [0.0, 1.0] but is {strength}"
+            )
+
+        # set timesteps
+        accepts_offset = "offset" in set(
+            inspect.signature(self.scheduler.set_timesteps).parameters.keys()
+        )
+        extra_set_kwargs = {}
+        offset = 0
+        if accepts_offset:
+            offset = 1
+            extra_set_kwargs["offset"] = 1
+
+        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
+
+        # preprocess image
+        init_image = preprocess_image(init_image).to(self.device)
+
+        # encode the init image into latents and scale the latents
+        init_latents = self.vae.encode(init_image).sample()
+        init_latents = 0.18215 * init_latents
+
+        # prepare init_latents noise to latents
+        init_latents = torch.cat([init_latents] * batch_size)
+        init_latents_orig = init_latents
+
+        # preprocess mask
+        mask = preprocess_mask(mask_image).to(self.device)
+        mask = torch.cat([mask] * batch_size)
+
+        # check sizes
+        if not mask.shape == init_latents.shape:
+            raise ValueError(f"The mask and init_image should be the same size!")
+
+        # get the original timestep using init_timestep
+        init_timestep = int(num_inference_steps * strength) + offset
+        init_timestep = min(init_timestep, num_inference_steps)
+        timesteps = self.scheduler.timesteps[-init_timestep]
+        timesteps = torch.tensor(
+            [timesteps] * batch_size, dtype=torch.long, device=self.device
+        )
+
+        # add noise to latents using the timesteps
+        noise = torch.randn(init_latents.shape, generator=generator, device=self.device)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timesteps)
+
+        # get prompt text embeddings
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_embeddings = self.text_encoder(text_input.input_ids.to(self.device))[0]
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            max_length = text_input.input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                [""] * batch_size,
+                padding="max_length",
+                max_length=max_length,
+                return_tensors="pt",
+            )
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(self.device)
+            )[0]
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+        accepts_eta = "eta" in set(
+            inspect.signature(self.scheduler.step).parameters.keys()
+        )
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        latents = init_latents
+        t_start = max(num_inference_steps - init_timestep + offset, 0)
+        for i, t in tqdm(enumerate(self.scheduler.timesteps[t_start:])):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = (
+                torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            )
+
+            # predict the noise residual
+            noise_pred = self.unet(
+                latent_model_input, t, encoder_hidden_states=text_embeddings
+            )["sample"]
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (
+                    noise_pred_text - noise_pred_uncond
+                )
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)[
+                "prev_sample"
+            ]
+
+            # masking
+            init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, t)
+            latents = (init_latents_proper * mask) + (latents * (1 - mask))
+
+        # scale and decode the image latents with vae
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents)
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+
+        # run safety checker
+        safety_cheker_input = self.feature_extractor(
+            self.numpy_to_pil(image), return_tensors="pt"
+        ).to(self.device)
+        image, has_nsfw_concept = self.safety_checker(
+            images=image, clip_input=safety_cheker_input.pixel_values
+        )
+
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        return {"sample": image, "nsfw_content_detected": has_nsfw_concept}

prototype/requirements.txt

@@ -0,0 +1,7 @@
+torch
+requests
+pillow
+diffusers
+gradio
+numpy
+tqdm

prototype/test.py

@@ -0,0 +1,12 @@
+import gradio as gr
+
+def greet(name, intensity):
+    return "Hello " * intensity + name + "!"
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
+)
+
+demo.launch()

prototype/utils.py

@@ -0,0 +1,13 @@
+def add_feature(image):
+    # inpainting features
+    pass
+
+
+def remove_feature(image):
+    # inpainting features
+    pass
+
+
+def enhance_feature(image):
+    # inpainting features
+    pass

setup.py

@@ -0,0 +1,44 @@
+# need to change 
+
+# import platform
+# import sys
+# from pathlib import Path
+
+# import pkg_resources
+# from setuptools import find_packages, setup
+
+
+# def read_version(fname="version.py"):
+#     exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
+#     return locals()["__version__"]
+
+
+# requirements = []
+# if sys.platform.startswith("linux") and platform.machine() == "x86_64":
+#     requirements.append("triton>=2.0.0,<3")
+
+# setup(
+#     name="openai-whisper",
+#     py_modules=["whisper"],
+#     version=read_version(),
+#     description="Robust Speech Recognition via Large-Scale Weak Supervision",
+#     long_description=open("README.md", encoding="utf-8").read(),
+#     long_description_content_type="text/markdown",
+#     readme="README.md",
+#     python_requires=">=3.8",
+#     author="OpenAI",
+#     url="https://github.com/openai/whisper",
+#     license="MIT",
+#     packages=find_packages(exclude=["tests*"]),
+#     install_requires=[
+#         str(r)
+#         for r in pkg_resources.parse_requirements(
+#             Path(__file__).with_name("requirements.txt").open()
+#         )
+#     ],
+#     entry_points={
+#         "console_scripts": ["whisper=whisper.transcribe:cli"],
+#     },
+#     include_package_data=True,
+#     extras_require={"dev": ["pytest", "scipy", "black", "flake8", "isort"]},
+# )

space/app.py

@@ -1,62 +0,0 @@
-import streamlit as st
-
-# Function for automatic option
-def process_automatic(image):
-    # Replace this with your actual processing logic for automatic option
-    return image
-
-# Function for inpainting option
-def process_inpainting(image, inpainting_option):
-    # Replace this with your actual processing logic for inpainting option
-    return image
-
-# Function for points option
-def process_points(image, points_option):
-    # Replace this with your actual processing logic for points option
-    return image
-
-# Main function to organize the Streamlit app
-def main():
-    # Set the title and page layout
-    st.set_page_config(page_title="Pearl's Virtual Staging", page_icon="🏡", layout="wide")
-
-    # Display the title
-    st.title("Pearl's Virtual Staging using Generative AI")
-    
-    option = st.selectbox("Choose an option", ["Automatic", "Inpainting", "Points"])
-
-    # Display inputs and process based on the chosen option
-    if option == "Automatic":
-        automatic_option()
-
-    elif option == "Inpainting":
-        inpainting_option()
-
-    elif option == "Points":
-        points_option()
-
-# Automatic option with one image input
-def automatic_option():
-    image_automatic = st.file_uploader("Upload an image", type=["jpg", "png"])
-    if image_automatic is not None:
-        # Process the image using a function
-        st.image(process_automatic(image_automatic))
-
-# Inpainting option with a select box for different inpainting functions
-def inpainting_option():
-    inpainting_option = st.selectbox("Choose an inpainting function", ["Function 1", "Function 2", "Function 3"])
-    image_inpainting = st.file_uploader("Upload an image", type=["jpg", "png"])
-    if image_inpainting is not None:
-        # Process the image based on the selected inpainting function
-        st.image(process_inpainting(image_inpainting, inpainting_option))
-
-# Points option with a select box for different point-related functions
-def points_option():
-    points_option = st.selectbox("Choose a points function", ["Function A", "Function B", "Function C"])
-    image_points = st.file_uploader("Upload an image", type=["jpg", "png"])
-    if image_points is not None:
-        # Process the image based on the selected points function
-        st.image(process_points(image_points, points_option))
-
-if __name__ == "__main__":
-    main()

tests/demo.py

@@ -0,0 +1,25 @@
+# test_demo.py
+import unittest
+
+def add_numbers(a, b):
+    return a + b
+
+def multiply_numbers(a, b):
+    return a * b
+
+
+
+class TestDemoFunctions(unittest.TestCase):
+
+    def test_add_numbers(self):
+        self.assertEqual(add_numbers(2, 3), 5)
+        self.assertEqual(add_numbers(-1, 1), 0)
+        # Add more test cases as needed
+
+    def test_multiply_numbers(self):
+        self.assertEqual(multiply_numbers(2, 3), 6)
+        self.assertEqual(multiply_numbers(-1, 1), -1)
+        # Add more test cases as needed
+
+if __name__ == '__main__':
+    unittest.main()

version.py

@@ -0,0 +1 @@
+__version__ = "20231117"