Duplicate from timboie/test

Co-authored-by: Timothy Boie <timboie@users.noreply.huggingface.co>

.gitattributes

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Dockerfile

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+FROM python:3.9.9-bullseye
+
+WORKDIR /src
+
+RUN apt-get update && \
+    apt-get install -y \
+    libgl1 libglib2.0-0
+
+COPY requirements.txt /src/
+
+RUN pip3 install -r requirements.txt
+
+COPY stable_diffusion_engine.py demo.py demo_web.py /src/
+COPY data/ /src/data/
+
+# download models
+RUN python3 demo.py --num-inference-steps 1 --prompt "test" --output /tmp/test.jpg
+
+ENTRYPOINT ["python3", "demo.py"]

LICENSE

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LICENSE_MODEL

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+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+CreativeML Open RAIL-M
+dated August 22, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
+
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+
+NOW THEREFORE, You and Licensor agree as follows:
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+
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+Use Restrictions
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README.md

@@ -0,0 +1,3 @@
+---
+duplicated_from: timboie/test
+---

demo.py

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+# -- coding: utf-8 --`
+import argparse
+import os
+# engine
+from stable_diffusion_engine import StableDiffusionEngine
+# scheduler
+from diffusers import LMSDiscreteScheduler, PNDMScheduler
+# utils
+import cv2
+import numpy as np
+
+
+def main(args):
+    if args.seed is not None:
+        np.random.seed(args.seed)
+    if args.init_image is None:
+        scheduler = LMSDiscreteScheduler(
+            beta_start=args.beta_start,
+            beta_end=args.beta_end,
+            beta_schedule=args.beta_schedule,
+            tensor_format="np"
+        )
+    else:
+        scheduler = PNDMScheduler(
+            beta_start=args.beta_start,
+            beta_end=args.beta_end,
+            beta_schedule=args.beta_schedule,
+            skip_prk_steps = True,
+            tensor_format="np"
+        )
+    engine = StableDiffusionEngine(
+        model = args.model,
+        scheduler = scheduler,
+        tokenizer = args.tokenizer
+    )
+    image = engine(
+        prompt = args.prompt,
+        init_image = None if args.init_image is None else cv2.imread(args.init_image),
+        mask = None if args.mask is None else cv2.imread(args.mask, 0),
+        strength = args.strength,
+        num_inference_steps = args.num_inference_steps,
+        guidance_scale = args.guidance_scale,
+        eta = args.eta
+    )
+    cv2.imwrite(args.output, image)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # pipeline configure
+    parser.add_argument("--model", type=str, default="bes-dev/stable-diffusion-v1-4-openvino", help="model name")
+    # randomizer params
+    parser.add_argument("--seed", type=int, default=None, help="random seed for generating consistent images per prompt")
+    # scheduler params
+    parser.add_argument("--beta-start", type=float, default=0.00085, help="LMSDiscreteScheduler::beta_start")
+    parser.add_argument("--beta-end", type=float, default=0.012, help="LMSDiscreteScheduler::beta_end")
+    parser.add_argument("--beta-schedule", type=str, default="scaled_linear", help="LMSDiscreteScheduler::beta_schedule")
+    # diffusion params
+    parser.add_argument("--num-inference-steps", type=int, default=32, help="num inference steps")
+    parser.add_argument("--guidance-scale", type=float, default=7.5, help="guidance scale")
+    parser.add_argument("--eta", type=float, default=0.0, help="eta")
+    # tokenizer
+    parser.add_argument("--tokenizer", type=str, default="openai/clip-vit-large-patch14", help="tokenizer")
+    # prompt
+    parser.add_argument("--prompt", type=str, default="Street-art painting of Emilia Clarke in style of Banksy, photorealism", help="prompt")
+    # img2img params
+    parser.add_argument("--init-image", type=str, default=None, help="path to initial image")
+    parser.add_argument("--strength", type=float, default=0.5, help="how strong the initial image should be noised [0.0, 1.0]")
+    # inpainting
+    parser.add_argument("--mask", type=str, default=None, help="mask of the region to inpaint on the initial image")
+    # output name
+    parser.add_argument("--output", type=str, default="output.png", help="output image name")
+    args = parser.parse_args()
+    main(args)

demo_web.py

@@ -0,0 +1,124 @@
+# -- coding: utf-8 --`
+import argparse
+import os
+import random
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+import numpy as np
+import cv2
+from  PIL import Image, ImageEnhance
+import numpy as np
+# engine
+from stable_diffusion_engine import StableDiffusionEngine
+# scheduler
+from diffusers import PNDMScheduler
+
+
+def run(engine):
+    with st.form(key="request"):
+        with st.sidebar:
+            prompt = st.text_area(label='Enter prompt')
+
+            with st.expander("Initial image"):
+                init_image = st.file_uploader("init_image", type=['jpg','png','jpeg'])
+                stroke_width = st.slider("stroke_width", 1, 100, 50)
+                stroke_color = st.color_picker("stroke_color", "#00FF00")
+                canvas_result = st_canvas(
+                    fill_color="rgb(0, 0, 0)",
+                    stroke_width = stroke_width,
+                    stroke_color = stroke_color,
+                    background_color = "#000000",
+                    background_image = Image.open(init_image) if init_image else None,
+                    height = 512,
+                    width = 512,
+                    drawing_mode = "freedraw",
+                    key = "canvas"
+                )
+
+            if init_image is not None:
+                init_image = cv2.cvtColor(np.array(Image.open(init_image)), cv2.COLOR_RGB2BGR)
+
+            if canvas_result.image_data is not None:
+                mask = cv2.cvtColor(canvas_result.image_data, cv2.COLOR_BGRA2GRAY)
+                mask[mask > 0] = 255
+            else:
+                mask = None
+
+            num_inference_steps = st.select_slider(
+                label='num_inference_steps',
+                options=range(1, 150),
+                value=32
+            )
+
+            guidance_scale = st.select_slider(
+                label='guidance_scale',
+                options=range(1, 21),
+                value=7
+            )
+
+            strength = st.slider(
+                label='strength',
+                min_value = 0.0,
+                max_value = 1.0,
+                value = 0.5
+            )
+
+            seed = st.number_input(
+                label='seed',
+                min_value = 0,
+                max_value = 2 ** 31,
+                value = random.randint(0, 2 ** 31)
+            )
+
+            generate = st.form_submit_button(label = 'Generate')
+
+        if prompt:
+            np.random.seed(seed)
+            image = engine(
+                prompt = prompt,
+                init_image = init_image,
+                mask = mask,
+                strength = strength,
+                num_inference_steps = num_inference_steps,
+                guidance_scale = guidance_scale
+            )
+            st.image(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), width=512)
+
+@st.cache(allow_output_mutation=True)
+def load_engine(args):
+    scheduler = PNDMScheduler(
+        beta_start=args.beta_start,
+        beta_end=args.beta_end,
+        beta_schedule=args.beta_schedule,
+        skip_prk_steps = True,
+        tensor_format="np"
+    )
+    engine = StableDiffusionEngine(
+        model = args.model,
+        scheduler = scheduler,
+        tokenizer = args.tokenizer
+    )
+    return engine
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # pipeline configure
+    parser.add_argument("--model", type=str, default="bes-dev/stable-diffusion-v1-4-openvino", help="model name")
+    # scheduler params
+    parser.add_argument("--beta-start", type=float, default=0.00085, help="LMSDiscreteScheduler::beta_start")
+    parser.add_argument("--beta-end", type=float, default=0.012, help="LMSDiscreteScheduler::beta_end")
+    parser.add_argument("--beta-schedule", type=str, default="scaled_linear", help="LMSDiscreteScheduler::beta_schedule")
+    # tokenizer
+    parser.add_argument("--tokenizer", type=str, default="openai/clip-vit-large-patch14", help="tokenizer")
+
+    try:
+        args = parser.parse_args()
+    except SystemExit as e:
+        # This exception will be raised if --help or invalid command line arguments
+        # are used. Currently streamlit prevents the program from exiting normally
+        # so we have to do a hard exit.
+        os._exit(e.code)
+
+    engine = load_engine(args)
+    run(engine)

docker-compose.yaml

@@ -0,0 +1,10 @@
+version: '3.9'
+services:
+  stable-diffusion:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    volumes:
+      # - /tmp/cache:/root/.cache
+      - /tmp/output:/tmp/output
+      # - /tmp/models:/root/models

requirements.txt

@@ -0,0 +1,12 @@
+numpy==1.19.5
+opencv-python==4.5.5.64
+transformers==4.16.2
+diffusers==0.2.4
+tqdm==4.64.0
+openvino==2022.1.0
+huggingface_hub==0.9.0
+scipy==1.9.0
+streamlit==1.12.0
+streamlit-drawable-canvas==0.8.0
+watchdog==2.1.9
+ftfy==6.1.1

stable_diffusion_engine.py

@@ -0,0 +1,212 @@
+import inspect
+import numpy as np
+# openvino
+from openvino.runtime import Core
+# tokenizer
+from transformers import CLIPTokenizer
+# utils
+from tqdm import tqdm
+from huggingface_hub import hf_hub_download
+from diffusers import LMSDiscreteScheduler, PNDMScheduler
+import cv2
+
+
+def result(var):
+    return next(iter(var.values()))
+
+
+class StableDiffusionEngine:
+    def __init__(
+            self,
+            scheduler,
+            model="bes-dev/stable-diffusion-v1-4-openvino",
+            tokenizer="openai/clip-vit-large-patch14",
+            device="CPU"
+    ):
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer)
+        self.scheduler = scheduler
+        # models
+        self.core = Core()
+        # text features
+        self._text_encoder = self.core.read_model(
+            hf_hub_download(repo_id=model, filename="text_encoder.xml"),
+            hf_hub_download(repo_id=model, filename="text_encoder.bin")
+        )
+        self.text_encoder = self.core.compile_model(self._text_encoder, device)
+        # diffusion
+        self._unet = self.core.read_model(
+            hf_hub_download(repo_id=model, filename="unet.xml"),
+            hf_hub_download(repo_id=model, filename="unet.bin")
+        )
+        self.unet = self.core.compile_model(self._unet, device)
+        self.latent_shape = tuple(self._unet.inputs[0].shape)[1:]
+        # decoder
+        self._vae_decoder = self.core.read_model(
+            hf_hub_download(repo_id=model, filename="vae_decoder.xml"),
+            hf_hub_download(repo_id=model, filename="vae_decoder.bin")
+        )
+        self.vae_decoder = self.core.compile_model(self._vae_decoder, device)
+        # encoder
+        self._vae_encoder = self.core.read_model(
+            hf_hub_download(repo_id=model, filename="vae_encoder.xml"),
+            hf_hub_download(repo_id=model, filename="vae_encoder.bin")
+        )
+        self.vae_encoder = self.core.compile_model(self._vae_encoder, device)
+        self.init_image_shape = tuple(self._vae_encoder.inputs[0].shape)[2:]
+
+    def _preprocess_mask(self, mask):
+        h, w = mask.shape
+        if h != self.init_image_shape[0] and w != self.init_image_shape[1]:
+            mask = cv2.resize(
+                mask,
+                (self.init_image_shape[1], self.init_image_shape[0]),
+                interpolation = cv2.INTER_NEAREST
+            )
+        mask = cv2.resize(
+            mask,
+            (self.init_image_shape[1] // 8, self.init_image_shape[0] // 8),
+            interpolation = cv2.INTER_NEAREST
+        )
+        mask = mask.astype(np.float32) / 255.0
+        mask = np.tile(mask, (4, 1, 1))
+        mask = mask[None].transpose(0, 1, 2, 3)
+        mask = 1 - mask
+        return mask
+
+    def _preprocess_image(self, image):
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        h, w = image.shape[1:]
+        if h != self.init_image_shape[0] and w != self.init_image_shape[1]:
+            image = cv2.resize(
+                image,
+                (self.init_image_shape[1], self.init_image_shape[0]),
+                interpolation=cv2.INTER_LANCZOS4
+            )
+        # normalize
+        image = image.astype(np.float32) / 255.0
+        image = 2.0 * image - 1.0
+        # to batch
+        image = image[None].transpose(0, 3, 1, 2)
+        return image
+
+    def _encode_image(self, init_image):
+        moments = result(self.vae_encoder.infer_new_request({
+            "init_image": self._preprocess_image(init_image)
+        }))
+        mean, logvar = np.split(moments, 2, axis=1)
+        std = np.exp(logvar * 0.5)
+        latent = (mean + std * np.random.randn(*mean.shape)) * 0.18215
+        return latent
+
+    def __call__(
+            self,
+            prompt,
+            init_image = None,
+            mask = None,
+            strength = 0.5,
+            num_inference_steps = 32,
+            guidance_scale = 7.5,
+            eta = 0.0
+    ):
+        # extract condition
+        tokens = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True
+        ).input_ids
+        text_embeddings = result(
+            self.text_encoder.infer_new_request({"tokens": np.array([tokens])})
+        )
+
+        # do classifier free guidance
+        if guidance_scale > 1.0:
+            tokens_uncond = self.tokenizer(
+                "",
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True
+            ).input_ids
+            uncond_embeddings = result(
+                self.text_encoder.infer_new_request({"tokens": np.array([tokens_uncond])})
+            )
+            text_embeddings = np.concatenate((uncond_embeddings, text_embeddings), axis=0)
+
+        # 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)
+
+        # initialize latent latent
+        if init_image is None:
+            latents = np.random.randn(*self.latent_shape)
+            init_timestep = num_inference_steps
+        else:
+            init_latents = self._encode_image(init_image)
+            init_timestep = int(num_inference_steps * strength) + offset
+            init_timestep = min(init_timestep, num_inference_steps)
+            timesteps = np.array([[self.scheduler.timesteps[-init_timestep]]]).astype(np.long)
+            noise = np.random.randn(*self.latent_shape)
+            latents = self.scheduler.add_noise(init_latents, noise, timesteps)[0]
+
+        if init_image is not None and mask is not None:
+            mask = self._preprocess_mask(mask)
+        else:
+            mask = None
+
+        # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
+        if isinstance(self.scheduler, LMSDiscreteScheduler):
+            latents = latents * self.scheduler.sigmas[0]
+
+        # 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
+
+        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 = np.stack([latents, latents], 0) if guidance_scale > 1.0 else latents[None]
+            if isinstance(self.scheduler, LMSDiscreteScheduler):
+                sigma = self.scheduler.sigmas[i]
+                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
+
+            # predict the noise residual
+            noise_pred = result(self.unet.infer_new_request({
+                "latent_model_input": latent_model_input,
+                "t": t,
+                "encoder_hidden_states": text_embeddings
+            }))
+
+            # perform guidance
+            if guidance_scale > 1.0:
+                noise_pred = noise_pred[0] + guidance_scale * (noise_pred[1] - noise_pred[0])
+
+            # compute the previous noisy sample x_t -> x_t-1
+            if isinstance(self.scheduler, LMSDiscreteScheduler):
+                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs)["prev_sample"]
+            else:
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)["prev_sample"]
+
+            # masking for inapinting
+            if mask is not None:
+                init_latents_proper = self.scheduler.add_noise(init_latents, noise, t)
+                latents = ((init_latents_proper * mask) + (latents * (1 - mask)))[0]
+
+        image = result(self.vae_decoder.infer_new_request({
+            "latents": np.expand_dims(latents, 0)
+        }))
+
+        # convert tensor to opencv's image format
+        image = (image / 2 + 0.5).clip(0, 1)
+        image = (image[0].transpose(1, 2, 0)[:, :, ::-1] * 255).astype(np.uint8)
+        return image