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| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| from .utils.schedulers import SCHEDULER_LIST, get_scheduler_list | |
| from .utils.prompt2prompt import generate | |
| from .utils.device import get_device | |
| from PIL import Image | |
| from .download import get_share_js, get_community_loading_icon, CSS | |
| IMG2IMG_MODEL_LIST = { | |
| "OpenJourney v4" : "prompthero/openjourney-v4", | |
| "StableDiffusion 1.5" : "runwayml/stable-diffusion-v1-5", | |
| "StableDiffusion 2.1" : "stabilityai/stable-diffusion-2-1", | |
| "DreamLike 1.0" : "dreamlike-art/dreamlike-diffusion-1.0", | |
| "DreamLike 2.0" : "dreamlike-art/dreamlike-photoreal-2.0", | |
| "DreamShaper" : "Lykon/DreamShaper", | |
| "NeverEnding-Dream" : "Lykon/NeverEnding-Dream" | |
| } | |
| class StableDiffusionImage2ImageGenerator: | |
| def __init__(self): | |
| self.pipe = None | |
| def load_model(self, model_path, scheduler): | |
| model_path = IMG2IMG_MODEL_LIST[model_path] | |
| if self.pipe is None: | |
| self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained( | |
| model_path, safety_checker=None, torch_dtype=torch.float32 | |
| ) | |
| device = get_device() | |
| self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler) | |
| self.pipe.to(device) | |
| self.pipe.enable_attention_slicing() | |
| return self.pipe | |
| def generate_image( | |
| self, | |
| image_path: str, | |
| model_path: str, | |
| prompt: str, | |
| negative_prompt: str, | |
| scheduler: str, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| seed_generator=0, | |
| ): | |
| pipe = self.load_model( | |
| model_path=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=1, | |
| num_inference_steps=num_inference_step, | |
| guidance_scale=guidance_scale, | |
| generator=generator, | |
| ).images | |
| return images | |
| def app(): | |
| demo = gr.Blocks(css=CSS) | |
| with demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| image2image_image_file = gr.Image( | |
| type="filepath", label="Upload",elem_id="image-upload-img2img" | |
| ).style(height=260) | |
| image2image_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Prompt, keywords that describe the changes you want to apply to your image", | |
| show_label=False, | |
| elem_id="prompt-text-input-img2img", | |
| value='' | |
| ) | |
| image2image_negative_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Negative Prompt, keywords that describe what you don't want in your image", | |
| show_label=False, | |
| elem_id = "negative-prompt-text-input-img2img", | |
| value='' | |
| ) | |
| # add button for generating a prompt from the prompt | |
| image2image_generate_prompt_button = gr.Button( | |
| label="Generate Prompt", | |
| type="primary", | |
| align="center", | |
| value = "Generate Prompt" | |
| ) | |
| # show a text box with the generated prompt | |
| image2image_generated_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Generated Prompt", | |
| label = "Generated Prompt", | |
| show_label=True, | |
| info="Auto generated prompts for inspiration.", | |
| ) | |
| image2image_model_path = gr.Dropdown( | |
| choices=list(IMG2IMG_MODEL_LIST.keys()), | |
| value=list(IMG2IMG_MODEL_LIST.keys())[0], | |
| label="Imaget2Image Model Selection", | |
| elem_id="model-dropdown-img2img", | |
| info="Select the model you want to use for image2image generation." | |
| ) | |
| image2image_scheduler = gr.Dropdown( | |
| choices=SCHEDULER_LIST, | |
| value=SCHEDULER_LIST[0], | |
| label="Scheduler", | |
| elem_id="scheduler-dropdown-img2img", | |
| info="Scheduler list for models. Different schdulers result in different outputs." | |
| ) | |
| image2image_seed_generator = gr.Slider( | |
| label="Seed(0 for random)", | |
| minimum=0, | |
| maximum=1000000, | |
| value=0, | |
| elem_id="seed-slider-img2img", | |
| info="Set the seed to a specific value to reproduce the results." | |
| ) | |
| image2image_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| elem_id = "guidance-scale-slider-img2img", | |
| info = "Guidance scale determines how much the prompt will affect the image. Higher the value, more the effect." | |
| ) | |
| image2image_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| elem_id = "num-inference-step-slider-img2img", | |
| info = "Number of inference step determines the quality of the image. Higher the number, better the quality." | |
| ) | |
| image2image_predict_button = gr.Button(value="Generate image") | |
| with gr.Column(): | |
| output_image = gr.Gallery( | |
| label="Generated images", | |
| show_label=False, | |
| elem_id="gallery-img2img", | |
| ).style(grid=(1, 2)) | |
| with gr.Group(elem_id="container-advanced-btns"): | |
| with gr.Group(elem_id="share-btn-container"): | |
| community_icon_html, loading_icon_html = get_community_loading_icon("img2img") | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Save artwork", elem_id="share-btn-img2img") | |
| # Create an html for describing the models | |
| gr.HTML( | |
| """ | |
| <div id="model-description-img2img"> | |
| <h3>Image2Image Models</h3> | |
| <p>Image2Image models are trained to generate images from a given prompt. The prompt should specify what to change in general for the provided image.</p> | |
| <p>For example with a butterfly image, the prompt can be "blue butterfly".</p> | |
| <p>Negative prompt can be used to specify what not to change in the image. For example, with the butterfly image, the negative prompt can be "dark blue, blurry image".</p> | |
| <hr> | |
| <p>Stable Diffusion 1.5 & 2.1: Default model for many tasks. </p> | |
| <p>OpenJourney v4: Generates fantasy themed images similar to the Midjourney model. </p> | |
| <p>Dreamlike Photoreal 1.0 & 2.0 is SD 1.5 that generates realistic images. </p> | |
| </div> | |
| """ | |
| ) | |
| image2image_predict_button.click( | |
| fn=StableDiffusionImage2ImageGenerator().generate_image, | |
| inputs=[ | |
| image2image_image_file, | |
| image2image_model_path, | |
| image2image_prompt, | |
| image2image_negative_prompt, | |
| image2image_scheduler, | |
| image2image_guidance_scale, | |
| image2image_num_inference_step, | |
| image2image_seed_generator, | |
| ], | |
| outputs=[output_image], | |
| ) | |
| image2image_generate_prompt_button.click( | |
| fn=generate, | |
| inputs=[image2image_prompt], | |
| outputs=[image2image_generated_prompt], | |
| ) | |
| return demo | |