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| import gradio as gr | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| from .utils.schedulers import SCHEDULER_LIST, get_scheduler_list | |
| from .utils.prompt2prompt import generate | |
| from .utils.device import get_device | |
| from .download import get_share_js, community_icon_html, loading_icon_html, CSS | |
| #--- create a download button that takes the output image from gradio and downloads it | |
| TEXT2IMG_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 StableDiffusionText2ImageGenerator: | |
| def __init__(self): | |
| self.pipe = None | |
| def load_model( | |
| self, | |
| model_path, | |
| scheduler | |
| ): | |
| model_path = TEXT2IMG_MODEL_LIST[model_path] | |
| if self.pipe is None: | |
| self.pipe = StableDiffusionPipeline.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, | |
| model_path: str, | |
| prompt: str, | |
| negative_prompt: str, | |
| scheduler: str, | |
| guidance_scale: int, | |
| num_inference_step: int, | |
| height: int, | |
| width: int, | |
| seed_generator=0, | |
| ): | |
| print("model_path", model_path) | |
| print("prompt", prompt) | |
| print("negative_prompt", negative_prompt) | |
| print("num_images_per_prompt", 1) | |
| print("scheduler", scheduler) | |
| print("guidance_scale", guidance_scale) | |
| print("num_inference_step", num_inference_step) | |
| print("height", height) | |
| print("width", width) | |
| print("seed_generator", seed_generator) | |
| 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) | |
| images = pipe( | |
| prompt=prompt, | |
| height=height, | |
| width=width, | |
| 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(username : str = "admin"): | |
| demo = gr.Blocks(css = CSS) | |
| with demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| text2image_prompt = gr.Textbox( | |
| lines=1, | |
| show_label=False, | |
| elem_id="prompt-text-input", | |
| value='', | |
| placeholder="Prompt, keywords that describe your image" | |
| ) | |
| text2image_negative_prompt = gr.Textbox( | |
| lines=1, | |
| show_label=False, | |
| elem_id = "negative-prompt-text-input", | |
| value='', | |
| placeholder="Negative Prompt, keywords that describe what you don't want in your image", | |
| ) | |
| # add button for generating a prompt from the prompt | |
| text2image_prompt_generate_button = gr.Button( | |
| label="Generate Prompt", | |
| type="primary", | |
| align="center", | |
| value = "Generate Prompt" | |
| ) | |
| # show a text box with the generated prompt | |
| text2image_prompt_generated_prompt = gr.Textbox( | |
| lines=1, | |
| placeholder="Generated Prompt", | |
| show_label=False, | |
| info="Auto generated prompts for inspiration.", | |
| ) | |
| text2image_model_path = gr.Dropdown( | |
| choices=list(TEXT2IMG_MODEL_LIST.keys()), | |
| value=list(TEXT2IMG_MODEL_LIST.keys())[0], | |
| label="Text2Image Model Selection", | |
| elem_id="model-dropdown", | |
| info="Select the model you want to use for text2image generation." | |
| ) | |
| text2image_scheduler = gr.Dropdown( | |
| choices=SCHEDULER_LIST, | |
| value=SCHEDULER_LIST[0], | |
| label="Scheduler", | |
| elem_id="scheduler-dropdown", | |
| info="Scheduler list for models. Different schdulers result in different outputs." | |
| ) | |
| text2image_size = gr.Slider( | |
| minimum=128, | |
| maximum=1280, | |
| step=32, | |
| value=768, | |
| label="Image Size", | |
| elem_id="image-size-slider", | |
| info = "Image size determines the height and width of the generated image. Higher the value, better the quality however slower the computation." | |
| ) | |
| text2image_seed_generator = gr.Slider( | |
| label="Seed(0 for random)", | |
| minimum=0, | |
| maximum=1000000, | |
| value=0, | |
| elem_id="seed-slider", | |
| info="Set the seed to a specific value to reproduce the results." | |
| ) | |
| text2image_guidance_scale = gr.Slider( | |
| minimum=0.1, | |
| maximum=15, | |
| step=0.1, | |
| value=7.5, | |
| label="Guidance Scale", | |
| elem_id = "guidance-scale-slider", | |
| info = "Guidance scale determines how much the prompt will affect the image. Higher the value, more the effect." | |
| ) | |
| text2image_num_inference_step = gr.Slider( | |
| minimum=1, | |
| maximum=100, | |
| step=1, | |
| value=50, | |
| label="Num Inference Step", | |
| elem_id = "num-inference-step-slider", | |
| info = "Number of inference step determines the quality of the image. Higher the number, better the quality." | |
| ) | |
| text2image_predict = gr.Button(value="Generate image") | |
| with gr.Column(): | |
| output_image = gr.Gallery( | |
| label="Generated images", | |
| show_label=False, | |
| elem_id="gallery", | |
| ).style(grid=(1, 2), height='auto') | |
| with gr.Group(elem_id="container-advanced-btns"): | |
| with gr.Group(elem_id="share-btn-container"): | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Save artwork", elem_id="share-btn") | |
| gr.HTML( | |
| """ | |
| <div id="model-description-text2img"> | |
| <h3>Text2Image Models</h3> | |
| <p>Text to image models will generate an image guided by the prompt that is provided</p> | |
| <p>A prompt should be specified with keywords that describe the image you want to generate.</p> | |
| <p>Negative prompt can be used to specify keywords that you don't want in your image such as "blood" or "violence".</p> | |
| <p>Example prompt: "A painting of a cat sitting on a chair, fantasy themed, starry background"</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> | |
| """ | |
| ) | |
| text2image_predict.click( | |
| fn=StableDiffusionText2ImageGenerator().generate_image, | |
| inputs=[ | |
| text2image_model_path, | |
| text2image_prompt, | |
| text2image_negative_prompt, | |
| text2image_scheduler, | |
| text2image_guidance_scale, | |
| text2image_num_inference_step, | |
| text2image_size, | |
| text2image_size, | |
| text2image_seed_generator, | |
| ], | |
| outputs=output_image, | |
| ) | |
| text2image_prompt_generate_button.click( | |
| fn=generate, | |
| inputs=[text2image_prompt], | |
| outputs=[text2image_prompt_generated_prompt], | |
| ) | |
| # share_button.click( | |
| # None, | |
| # [], | |
| # [], | |
| # _js=get_share_js(), | |
| # ) | |
| # autoclik the share button | |
| return demo |