update

diffusion_webui/__init__.py

@@ -1,17 +0,0 @@
-from diffusion_webui.diffusion_models.controlnet_inpaint_pipeline import (
-    StableDiffusionControlNetInpaintGenerator,
-)
-from diffusion_webui.diffusion_models.controlnet_pipeline import (
-    StableDiffusionControlNetGenerator,
-)
-from diffusion_webui.diffusion_models.img2img_app import (
-    StableDiffusionImage2ImageGenerator,
-)
-from diffusion_webui.diffusion_models.inpaint_app import (
-    StableDiffusionInpaintGenerator,
-)
-from diffusion_webui.diffusion_models.text2img_app import (
-    StableDiffusionText2ImageGenerator,
-)
-
-__version__ = "2.5.0"

diffusion_webui/diffusion_models/base_controlnet_pipeline.py

@@ -1,31 +0,0 @@
-class ControlnetPipeline:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path: str, controlnet_model_path: str):
-        raise NotImplementedError()
-
-    def load_image(self, image_path: str):
-        raise NotImplementedError()
-
-    def controlnet_preprocces(self, read_image: str):
-        raise NotImplementedError()
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        controlnet_conditioning_scale: int,
-        scheduler: str,
-        seed_generator: int,
-    ):
-        raise NotImplementedError()
-
-    def web_interface():
-        raise NotImplementedError()

diffusion_webui/diffusion_models/controlnet_inpaint_pipeline.py

@@ -1,258 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline
-from PIL import Image
-
-from diffusion_webui.diffusion_models.base_controlnet_pipeline import (
-    ControlnetPipeline,
-)
-from diffusion_webui.utils.model_list import (
-    controlnet_model_list,
-    stable_model_list,
-)
-from diffusion_webui.utils.preprocces_utils import PREPROCCES_DICT
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-class StableDiffusionControlNetInpaintGenerator(ControlnetPipeline):
-    def __init__(self):
-        super().__init__()
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = (
-                StableDiffusionControlNetInpaintPipeline.from_pretrained(
-                    pretrained_model_name_or_path=stable_model_path,
-                    controlnet=controlnet,
-                    safety_checker=None,
-                    torch_dtype=torch.float16,
-                )
-            )
-            
-            self.pipe.model_name = stable_model_path
-            self.pipe.scheduler_name = scheduler
-            self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def load_image(self, image):
-        image = np.array(image)
-        image = Image.fromarray(image)
-        return image
-
-    def controlnet_preprocces(
-        self,
-        read_image: str,
-        preprocces_type: str,
-    ):
-        processed_image = PREPROCCES_DICT[preprocces_type](read_image)
-        return processed_image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        height: int,
-        width: int,
-        strength: int,
-        guess_mode: bool,
-        guidance_scale: int,
-        num_inference_step: int,
-        controlnet_conditioning_scale: int,
-        scheduler: str,
-        seed_generator: int,
-        preprocces_type: str,
-    ):
-        normal_image = image_path["image"].convert("RGB").resize((512, 512))
-        mask_image = image_path["mask"].convert("RGB").resize((512, 512))
-
-        normal_image = self.load_image(image=normal_image)
-        mask_image = self.load_image(image=mask_image)
-
-        control_image = self.controlnet_preprocces(
-            read_image=normal_image, preprocces_type=preprocces_type
-        )
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=normal_image,
-            height=height,
-            width=width,
-            mask_image=mask_image,
-            strength=strength,
-            guess_mode=guess_mode,
-            control_image=control_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_inpaint_image_path = gr.Image(
-                        source="upload",
-                        tool="sketch",
-                        elem_id="image_upload",
-                        type="pil",
-                        label="Upload",
-                    ).style(height=260)
-
-                    controlnet_inpaint_prompt = gr.Textbox(
-                        lines=1, placeholder="Prompt", show_label=False
-                    )
-                    controlnet_inpaint_negative_prompt = gr.Textbox(
-                        lines=1, placeholder="Negative Prompt", show_label=False
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_inpaint_stable_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Path",
-                            )
-                            controlnet_inpaint_preprocces_type = gr.Dropdown(
-                                choices=list(PREPROCCES_DICT.keys()),
-                                value=list(PREPROCCES_DICT.keys())[0],
-                                label="Preprocess Type",
-                            )
-                            controlnet_inpaint_conditioning_scale = gr.Slider(
-                                minimum=0.0,
-                                maximum=1.0,
-                                step=0.1,
-                                value=1.0,
-                                label="ControlNet Conditioning Scale",
-                            )
-                            controlnet_inpaint_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_inpaint_height = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Height",
-                            )
-                            controlnet_inpaint_width = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Width",
-                            )
-                            controlnet_inpaint_guess_mode = gr.Checkbox(
-                                label="Guess Mode"
-                            )
-
-                        with gr.Column():
-                            controlnet_inpaint_model_path = gr.Dropdown(
-                                choices=controlnet_model_list,
-                                value=controlnet_model_list[0],
-                                label="ControlNet Model Path",
-                            )
-                            controlnet_inpaint_scheduler = gr.Dropdown(
-                                choices=list(SCHEDULER_MAPPING.keys()),
-                                value=list(SCHEDULER_MAPPING.keys())[0],
-                                label="Scheduler",
-                            )
-                            controlnet_inpaint_strength = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Strength",
-                            )
-                            controlnet_inpaint_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=150,
-                                step=1,
-                                value=30,
-                                label="Num Inference Step",
-                            )
-                            controlnet_inpaint_num_images_per_prompt = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=4,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                            )
-                            controlnet_inpaint_seed_generator = gr.Slider(
-                                minimum=0,
-                                maximum=1000000,
-                                step=1,
-                                value=0,
-                                label="Seed(0 for random)",
-                            )
-
-                    # Button to generate the image
-                    controlnet_inpaint_predict_button = gr.Button(
-                        value="Generate Image"
-                    )
-
-                with gr.Column():
-                    # Gallery to display the generated images
-                    controlnet_inpaint_output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-        controlnet_inpaint_predict_button.click(
-            fn=StableDiffusionControlNetInpaintGenerator().generate_image,
-            inputs=[
-                controlnet_inpaint_image_path,
-                controlnet_inpaint_stable_model_path,
-                controlnet_inpaint_model_path,
-                controlnet_inpaint_prompt,
-                controlnet_inpaint_negative_prompt,
-                controlnet_inpaint_num_images_per_prompt,
-                controlnet_inpaint_height,
-                controlnet_inpaint_width,
-                controlnet_inpaint_strength,
-                controlnet_inpaint_guess_mode,
-                controlnet_inpaint_guidance_scale,
-                controlnet_inpaint_num_inference_step,
-                controlnet_inpaint_conditioning_scale,
-                controlnet_inpaint_scheduler,
-                controlnet_inpaint_seed_generator,
-                controlnet_inpaint_preprocces_type,
-            ],
-            outputs=[controlnet_inpaint_output_image],
-        )

diffusion_webui/diffusion_models/controlnet_pipeline.py

@@ -1,262 +0,0 @@
-import gradio as gr
-import torch
-import cv2
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
-from PIL import Image
-
-from diffusion_webui.diffusion_models.base_controlnet_pipeline import (
-    ControlnetPipeline,
-)
-from diffusion_webui.utils.model_list import (
-    controlnet_model_list,
-    stable_model_list,
-)
-from diffusion_webui.utils.preprocces_utils import PREPROCCES_DICT
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-stable_model_list = [
-    "runwayml/stable-diffusion-v1-5",
-    "dreamlike-art/dreamlike-diffusion-1.0",
-    "kadirnar/maturemalemix_v0",
-    "kadirnar/DreamShaper_v6"
-]
-
-stable_inpiant_model_list = [
-    "stabilityai/stable-diffusion-2-inpainting",
-    "runwayml/stable-diffusion-inpainting",
-    "saik0s/realistic_vision_inpainting",
-]
-
-controlnet_model_list = [
-    "lllyasviel/control_v11p_sd15_canny",
-    "lllyasviel/control_v11f1p_sd15_depth",
-    "lllyasviel/control_v11p_sd15_openpose",
-    "lllyasviel/control_v11p_sd15_scribble",
-    "lllyasviel/control_v11p_sd15_mlsd",
-    "lllyasviel/control_v11e_sd15_shuffle",
-    "lllyasviel/control_v11e_sd15_ip2p",
-    "lllyasviel/control_v11p_sd15_lineart",
-    "lllyasviel/control_v11p_sd15s2_lineart_anime",
-    "lllyasviel/control_v11p_sd15_softedge",
-]
-
-class StableDiffusionControlNetGenerator(ControlnetPipeline):
-    def __init__(self):
-        self.pipe = None
-        
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
-                pretrained_model_name_or_path=stable_model_path,
-                controlnet=controlnet,
-                safety_checker=None,
-                torch_dtype=torch.float16,
-            )
-            self.pipe.model_name = stable_model_path
-            self.pipe.scheduler_name = scheduler
-            
-            self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-            self.pipe.scheduler_name = scheduler
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-    
-        return self.pipe
-
-
-    def controlnet_preprocces(
-        self,
-        read_image: str,
-        preprocces_type: str,
-    ):
-        processed_image = PREPROCCES_DICT[preprocces_type](read_image)
-        return processed_image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        height: int,
-        width: int,
-        guess_mode: bool,
-        controlnet_conditioning_scale: int,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        scheduler: str,
-        seed_generator: int,
-        preprocces_type: str,
-    ):
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-        if preprocces_type== "ScribbleXDOG":
-            read_image = cv2.imread(image_path)
-            controlnet_image = self.controlnet_preprocces(read_image=read_image, preprocces_type=preprocces_type)[0]
-            controlnet_image = Image.fromarray(controlnet_image)
-            
-        elif preprocces_type== "None":
-            controlnet_image = self.controlnet_preprocces(read_image=image_path, preprocces_type=preprocces_type)
-        else:
-            read_image = Image.open(image_path)
-            controlnet_image = self.controlnet_preprocces(read_image=read_image, preprocces_type=preprocces_type)
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-        
-
-        output = pipe(
-            prompt=prompt,
-            height=height,
-            width=width,
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-            guess_mode=guess_mode,
-            image=controlnet_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_image_path = gr.Image(
-                        type="filepath", label="Image"
-                    ).style(height=260)
-                    controlnet_prompt = gr.Textbox(
-                        lines=1, placeholder="Prompt", show_label=False
-                    )
-                    controlnet_negative_prompt = gr.Textbox(
-                        lines=1, placeholder="Negative Prompt", show_label=False
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_stable_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Path",
-                            )
-                            controlnet_preprocces_type = gr.Dropdown(
-                                choices=list(PREPROCCES_DICT.keys()),
-                                value=list(PREPROCCES_DICT.keys())[0],
-                                label="Preprocess Type",
-                            )
-                            controlnet_conditioning_scale = gr.Slider(
-                                minimum=0.0,
-                                maximum=1.0,
-                                step=0.1,
-                                value=1.0,
-                                label="ControlNet Conditioning Scale",
-                            )
-                            controlnet_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_height = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Height",
-                            )
-                            controlnet_width = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Width",
-                            )
-
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_model_path = gr.Dropdown(
-                                    choices=controlnet_model_list,
-                                    value=controlnet_model_list[0],
-                                    label="ControlNet Model Path",
-                                )
-                                controlnet_scheduler = gr.Dropdown(
-                                    choices=list(SCHEDULER_MAPPING.keys()),
-                                    value=list(SCHEDULER_MAPPING.keys())[0],
-                                    label="Scheduler",
-                                )
-                                controlnet_num_inference_step = gr.Slider(
-                                    minimum=1,
-                                    maximum=150,
-                                    step=1,
-                                    value=30,
-                                    label="Num Inference Step",
-                                )
-
-                                controlnet_num_images_per_prompt = gr.Slider(
-                                    minimum=1,
-                                    maximum=4,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                                controlnet_seed_generator = gr.Slider(
-                                    minimum=0,
-                                    maximum=1000000,
-                                    step=1,
-                                    value=0,
-                                    label="Seed(0 for random)",
-                                )
-                                controlnet_guess_mode = gr.Checkbox(
-                                    label="Guess Mode"
-                                )
-
-                    # Button to generate the image
-                    predict_button = gr.Button(value="Generate Image")
-
-                with gr.Column():
-                    # Gallery to display the generated images
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-        predict_button.click(
-            fn=StableDiffusionControlNetGenerator().generate_image,
-            inputs=[
-                controlnet_image_path,
-                controlnet_stable_model_path,
-                controlnet_model_path,
-                controlnet_height,
-                controlnet_width,
-                controlnet_guess_mode,
-                controlnet_conditioning_scale,
-                controlnet_prompt,
-                controlnet_negative_prompt,
-                controlnet_num_images_per_prompt,
-                controlnet_guidance_scale,
-                controlnet_num_inference_step,
-                controlnet_scheduler,
-                controlnet_seed_generator,
-                controlnet_preprocces_type,
-            ],
-            outputs=[output_image],
-        )

diffusion_webui/diffusion_models/img2img_app.py

@@ -1,155 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionImg2ImgPipeline
-from PIL import Image
-
-from diffusion_webui.utils.model_list import stable_model_list
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-class StableDiffusionImage2ImageGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path, scheduler):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-                stable_model_path, safety_checker=None, torch_dtype=torch.float16
-            )
-            
-            self.pipe.model_name = stable_model_path
-            self.pipe.scheduler_name = scheduler
-            self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        scheduler: str,
-        guidance_scale: int,
-        num_inference_step: int,
-        seed_generator=0,
-    ):
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            scheduler=scheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        image = Image.open(image_path)
-        images = pipe(
-            prompt,
-            image=image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return images
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    image2image_image_file = gr.Image(
-                        type="filepath", label="Image"
-                    ).style(height=260)
-
-                    image2image_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Prompt",
-                        show_label=False,
-                    )
-
-                    image2image_negative_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Negative Prompt",
-                        show_label=False,
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            image2image_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Id",
-                            )
-
-                            image2image_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            image2image_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                image2image_scheduler = gr.Dropdown(
-                                    choices=list(SCHEDULER_MAPPING.keys()),
-                                    value=list(SCHEDULER_MAPPING.keys())[0],
-                                    label="Scheduler",
-                            )
-                                image2image_num_images_per_prompt = gr.Slider(
-                                    minimum=1,
-                                    maximum=4,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-
-                                image2image_seed_generator = gr.Slider(
-                                    minimum=0,
-                                    maximum=1000000,
-                                    step=1,
-                                    value=0,
-                                    label="Seed(0 for random)",
-                                )
-
-                    image2image_predict_button = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-        image2image_predict_button.click(
-            fn=StableDiffusionImage2ImageGenerator().generate_image,
-            inputs=[
-                image2image_image_file,
-                image2image_model_path,
-                image2image_prompt,
-                image2image_negative_prompt,
-                image2image_num_images_per_prompt,
-                image2image_scheduler,
-                image2image_guidance_scale,
-                image2image_num_inference_step,
-                image2image_seed_generator,
-            ],
-            outputs=[output_image],
-        )

diffusion_webui/diffusion_models/inpaint_app.py

@@ -1,149 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import DiffusionPipeline
-
-from diffusion_webui.utils.model_list import stable_inpiant_model_list
-
-
-class StableDiffusionInpaintGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path):
-        if self.pipe is None or self.pipe.model_name != stable_model_path:
-            self.pipe = DiffusionPipeline.from_pretrained(
-                stable_model_path, revision="fp16", torch_dtype=torch.float16
-            )
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-            self.pipe.model_name = stable_model_path
-            
-            
-        return self.pipe
-
-    def generate_image(
-        self,
-        pil_image: str,
-        stable_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        seed_generator=0,
-    ):
-        image = pil_image["image"].convert("RGB").resize((512, 512))
-        mask_image = pil_image["mask"].convert("RGB").resize((512, 512))
-        pipe = self.load_model(stable_model_path)
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=image,
-            mask_image=mask_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    stable_diffusion_inpaint_image_file = gr.Image(
-                        source="upload",
-                        tool="sketch",
-                        elem_id="image_upload",
-                        type="pil",
-                        label="Upload",
-                    ).style(height=260)
-
-                    stable_diffusion_inpaint_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Prompt",
-                        show_label=False,
-                    )
-
-                    stable_diffusion_inpaint_negative_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Negative Prompt",
-                        show_label=False,
-                    )
-                    stable_diffusion_inpaint_model_id = gr.Dropdown(
-                        choices=stable_inpiant_model_list,
-                        value=stable_inpiant_model_list[0],
-                        label="Inpaint Model Id",
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            stable_diffusion_inpaint_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-
-                            stable_diffusion_inpaint_num_inference_step = (
-                                gr.Slider(
-                                    minimum=1,
-                                    maximum=100,
-                                    step=1,
-                                    value=50,
-                                    label="Num Inference Step",
-                                )
-                            )
-
-                        with gr.Row():
-                            with gr.Column():
-                                stable_diffusion_inpiant_num_images_per_prompt = gr.Slider(
-                                    minimum=1,
-                                    maximum=4,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                                stable_diffusion_inpaint_seed_generator = (
-                                    gr.Slider(
-                                        minimum=0,
-                                        maximum=1000000,
-                                        step=1,
-                                        value=0,
-                                        label="Seed(0 for random)",
-                                    )
-                                )
-
-                    stable_diffusion_inpaint_predict = gr.Button(
-                        value="Generator"
-                    )
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-            stable_diffusion_inpaint_predict.click(
-                fn=StableDiffusionInpaintGenerator().generate_image,
-                inputs=[
-                    stable_diffusion_inpaint_image_file,
-                    stable_diffusion_inpaint_model_id,
-                    stable_diffusion_inpaint_prompt,
-                    stable_diffusion_inpaint_negative_prompt,
-                    stable_diffusion_inpiant_num_images_per_prompt,
-                    stable_diffusion_inpaint_guidance_scale,
-                    stable_diffusion_inpaint_num_inference_step,
-                    stable_diffusion_inpaint_seed_generator,
-                ],
-                outputs=[output_image],
-            )

diffusion_webui/diffusion_models/text2img_app.py

@@ -1,173 +0,0 @@
-import gradio as gr
-import torch
-from diffusers import StableDiffusionPipeline,DiffusionPipeline
-
-from diffusion_webui.utils.model_list import stable_model_list
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-class StableDiffusionText2ImageGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(
-        self,
-        stable_model_path,
-        scheduler,
-    ):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            if stable_model_path == "stabilityai/stable-diffusion-xl-base-0.9":
-                self.pipe = DiffusionPipeline.from_pretrained(
-                    stable_model_path, safety_checker=None, torch_dtype=torch.float16
-                )
-            else:
-                self.pipe = StableDiffusionPipeline.from_pretrained(
-                    stable_model_path, safety_checker=None, torch_dtype=torch.float16
-                )
-
-        self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-        self.pipe.model_name = stable_model_path
-        self.pipe.scheduler_name = scheduler
-
-        return self.pipe
-
-    def generate_image(
-        self,
-        stable_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        scheduler: str,
-        guidance_scale: int,
-        num_inference_step: int,
-        height: int,
-        width: int,
-        seed_generator=0,
-    ):
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            scheduler=scheduler,
-        )
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        images = pipe(
-            prompt=prompt,
-            height=height,
-            width=width,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return images
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    text2image_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Prompt",
-                        show_label=False,
-                    )
-
-                    text2image_negative_prompt = gr.Textbox(
-                        lines=1,
-                        placeholder="Negative Prompt",
-                        show_label=False,
-                    )
-                    with gr.Row():
-                        with gr.Column():
-                            text2image_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Text-Image Model Id",
-                            )
-
-                            text2image_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-
-                            text2image_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-                            text2image_num_images_per_prompt = gr.Slider(
-                                minimum=1,
-                                maximum=4,
-                                step=1,
-                                value=1,
-                                label="Number Of Images",
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                text2image_scheduler = gr.Dropdown(
-                                    choices=list(SCHEDULER_MAPPING.keys()),
-                                    value=list(SCHEDULER_MAPPING.keys())[0],
-                                    label="Scheduler",
-                            )
-
-                                text2image_height = gr.Slider(
-                                    minimum=128,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Image Height",
-                                )
-
-                                text2image_width = gr.Slider(
-                                    minimum=128,
-                                    maximum=1280,
-                                    step=32,
-                                    value=512,
-                                    label="Image Width",
-                                )
-                                text2image_seed_generator = gr.Slider(
-                                    label="Seed(0 for random)",
-                                    minimum=0,
-                                    maximum=1000000,
-                                    value=0,
-                                )
-                    text2image_predict = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2), height=200)
-
-            text2image_predict.click(
-                fn=StableDiffusionText2ImageGenerator().generate_image,
-                inputs=[
-                    text2image_model_path,
-                    text2image_prompt,
-                    text2image_negative_prompt,
-                    text2image_num_images_per_prompt,
-                    text2image_scheduler,
-                    text2image_guidance_scale,
-                    text2image_num_inference_step,
-                    text2image_height,
-                    text2image_width,
-                    text2image_seed_generator,
-                ],
-                outputs=output_image,
-            )

diffusion_webui/utils/data_utils.py

@@ -1,12 +0,0 @@
-from PIL import Image
-
-
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
-
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid

diffusion_webui/utils/model_list.py

@@ -1,25 +0,0 @@
-stable_model_list = [
-    "runwayml/stable-diffusion-v1-5",
-    "SG161222/Realistic_Vision_V2.0",
-    "stablediffusionapi/cyberrealistic",
-    "SG161222/Realistic_Vision_V5.1_noVAE",
-]
-
-stable_inpiant_model_list = [
-    "kadirnar/Realistic51-Inpaint",
-    "stabilityai/stable-diffusion-2-inpainting",
-    "runwayml/stable-diffusion-inpainting",
-]
-
-controlnet_model_list = [
-    "lllyasviel/control_v11p_sd15_canny",
-    "lllyasviel/control_v11f1p_sd15_depth",
-    "lllyasviel/control_v11p_sd15_openpose",
-    "lllyasviel/control_v11p_sd15_scribble",
-    "lllyasviel/control_v11p_sd15_mlsd",
-    "lllyasviel/control_v11e_sd15_shuffle",
-    "lllyasviel/control_v11e_sd15_ip2p",
-    "lllyasviel/control_v11p_sd15_lineart",
-    "lllyasviel/control_v11p_sd15s2_lineart_anime",
-    "lllyasviel/control_v11p_sd15_softedge",
-]

diffusion_webui/utils/preprocces_utils.py

@@ -1,94 +0,0 @@
-from controlnet_aux import (
-    CannyDetector,
-    ContentShuffleDetector,
-    HEDdetector,
-    LineartAnimeDetector,
-    LineartDetector,
-    MediapipeFaceDetector,
-    MidasDetector,
-    MLSDdetector,
-    NormalBaeDetector,
-    OpenposeDetector,
-    PidiNetDetector,
-    SamDetector,
-)
-
-import numpy as np
-import cv2
-
-def pad64(x):
-    return int(np.ceil(float(x) / 64.0) * 64 - x)
-
-def HWC3(x):
-    assert x.dtype == np.uint8
-    if x.ndim == 2:
-        x = x[:, :, None]
-    assert x.ndim == 3
-    H, W, C = x.shape
-    assert C == 1 or C == 3 or C == 4
-    if C == 3:
-        return x
-    if C == 1:
-        return np.concatenate([x, x, x], axis=2)
-    if C == 4:
-        color = x[:, :, 0:3].astype(np.float32)
-        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
-        y = color * alpha + 255.0 * (1.0 - alpha)
-        y = y.clip(0, 255).astype(np.uint8)
-        return y
-        
-def safer_memory(x):
-    return np.ascontiguousarray(x.copy()).copy()
-
-
-def resize_image_with_pad(input_image, resolution, skip_hwc3=False):
-    if skip_hwc3:
-        img = input_image
-    else:
-        img = HWC3(input_image)
-        
-    H_raw, W_raw, _ = img.shape
-    k = float(resolution) / float(min(H_raw, W_raw))
-    interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA
-    H_target = int(np.round(float(H_raw) * k))
-    W_target = int(np.round(float(W_raw) * k))
-    img = cv2.resize(img, (W_target, H_target), interpolation=interpolation)
-    H_pad, W_pad = pad64(H_target), pad64(W_target)
-    img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode='edge')
-
-    def remove_pad(x):
-        return safer_memory(x[:H_target, :W_target])
-
-    return safer_memory(img_padded), remove_pad
-
-
-def scribble_xdog(img, res=512, thr_a=32, **kwargs):
-    img, remove_pad = resize_image_with_pad(img, res)
-    g1 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 0.5)
-    g2 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 5.0)
-    dog = (255 - np.min(g2 - g1, axis=2)).clip(0, 255).astype(np.uint8)
-    result = np.zeros_like(img, dtype=np.uint8)
-    result[2 * (255 - dog) > thr_a] = 255
-    return remove_pad(result), True
-
-def none_preprocces(image_path:str):
-    return Image.open(image_path)
-
-PREPROCCES_DICT = {
-    "Hed": HEDdetector.from_pretrained("lllyasviel/Annotators"),
-    "Midas": MidasDetector.from_pretrained("lllyasviel/Annotators"),
-    "MLSD": MLSDdetector.from_pretrained("lllyasviel/Annotators"),
-    "Openpose": OpenposeDetector.from_pretrained("lllyasviel/Annotators"),
-    "PidiNet": PidiNetDetector.from_pretrained("lllyasviel/Annotators"),
-    "NormalBae": NormalBaeDetector.from_pretrained("lllyasviel/Annotators"),
-    "Lineart": LineartDetector.from_pretrained("lllyasviel/Annotators"),
-    "LineartAnime": LineartAnimeDetector.from_pretrained(
-        "lllyasviel/Annotators"
-    ),
-    "Canny": CannyDetector(),
-    "ContentShuffle": ContentShuffleDetector(),
-    "MediapipeFace": MediapipeFaceDetector(),
-    "ScribbleXDOG": scribble_xdog,
-    "None": none_preprocces
-}
-    

diffusion_webui/utils/scheduler_list.py

@@ -1,39 +0,0 @@
-from diffusers import (
-    DDIMScheduler,
-    DDPMScheduler,
-    DEISMultistepScheduler,
-    DPMSolverMultistepScheduler,
-    DPMSolverSinglestepScheduler,
-    EulerAncestralDiscreteScheduler,
-    EulerDiscreteScheduler,
-    HeunDiscreteScheduler,
-    KDPM2AncestralDiscreteScheduler,
-    KDPM2DiscreteScheduler,
-    PNDMScheduler,
-    UniPCMultistepScheduler,
-)
-
-SCHEDULER_MAPPING = {
-    "DDIM": DDIMScheduler,
-    "DDPMScheduler": DDPMScheduler,
-    "DEISMultistep": DEISMultistepScheduler,
-    "DPMSolverMultistep": DPMSolverMultistepScheduler,
-    "DPMSolverSinglestep": DPMSolverSinglestepScheduler,
-    "EulerAncestralDiscrete": EulerAncestralDiscreteScheduler,
-    "EulerDiscrete": EulerDiscreteScheduler,
-    "HeunDiscrete": HeunDiscreteScheduler,
-    "KDPM2AncestralDiscrete": KDPM2AncestralDiscreteScheduler,
-    "KDPM2Discrete": KDPM2DiscreteScheduler,
-    "PNDMScheduler": PNDMScheduler,
-    "UniPCMultistep": UniPCMultistepScheduler,
-}
-
-
-def get_scheduler(pipe, scheduler):
-    if scheduler in SCHEDULER_MAPPING:
-        SchedulerClass = SCHEDULER_MAPPING[scheduler]
-        pipe.scheduler = SchedulerClass.from_config(pipe.scheduler.config)
-    else:
-        raise ValueError(f"Invalid scheduler name {scheduler}")
-
-    return pipe