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