# tabs/image_tab.py import gradio as gr from modules.events.flux_events import * from modules.events.sdxl_events import * from modules.helpers.common_helpers import * from modules.helpers.flux_helpers import * from modules.helpers.sdxl_helpers import * from config import flux_models, sdxl_models, loras def image_tab(): with gr.Tab(label="🖼️ Image"): with gr.Tabs(): flux_tab() sdxl_tab() def flux_tab(): with gr.Tab("Flux"): with gr.Row(): with gr.Column(): with gr.Group() as image_options: model = gr.Dropdown(label="Models", choices=flux_models, value=flux_models[0], interactive=True) prompt = gr.Textbox(lines=5, label="Prompt") fast_generation = gr.Checkbox(label="Fast Generation (Hyper-SD) 🧪") with gr.Accordion("Loras", open=True): # Lora Gallery lora_gallery = gr.Gallery( label="Gallery", value=[(lora['image'], lora['title']) for lora in loras], allow_preview=False, columns=3, rows=3, type="pil" ) with gr.Group(): with gr.Column(): with gr.Row(): custom_lora = gr.Textbox(label="Custom Lora", info="Enter a Huggingface repo path") selected_lora = gr.Textbox(label="Selected Lora", info="Choose from the gallery or enter a custom LoRA") custom_lora_info = gr.HTML(visible=False) add_lora = gr.Button(value="Add LoRA") enabled_loras = gr.State(value=[]) with gr.Group(): with gr.Row(): for i in range(6): # only support max 6 loras due to inference time with gr.Column(): with gr.Column(scale=2): globals()[f"lora_slider_{i}"] = gr.Slider(label=f"LoRA {i+1}", minimum=0, maximum=1, step=0.01, value=0.8, visible=False, interactive=True) with gr.Column(): globals()[f"lora_remove_{i}"] = gr.Button(value="Remove LoRA", visible=False) with gr.Accordion("Embeddings", open=False): # Embeddings gr.Label("To be implemented") with gr.Accordion("Image Options"): # Image Options with gr.Tabs(): image_options = { "img2img": "Upload Image", "inpaint": "Upload Image", "canny": "Upload Image", "pose": "Upload Image", "depth": "Upload Image", } for image_option, label in image_options.items(): with gr.Tab(image_option): if not image_option in ['inpaint', 'scribble']: globals()[f"{image_option}_image"] = gr.Image(label=label, type="pil") elif image_option in ['inpaint', 'scribble']: globals()[f"{image_option}_image"] = gr.ImageEditor( label=label, image_mode='RGB', layers=False, brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed") if image_option == 'inpaint' else gr.Brush(), interactive=True, type="pil", ) # Image Strength (Co-relates to controlnet strength, strength for img2img n inpaint) globals()[f"{image_option}_strength"] = gr.Slider(label="Strength", minimum=0, maximum=1, step=0.01, value=1.0, interactive=True) resize_mode = gr.Radio( label="Resize Mode", choices=["crop and resize", "resize only", "resize and fill"], value="resize and fill", interactive=True ) with gr.Column(): with gr.Group(): output_images = gr.Gallery( label="Output Images", value=[], allow_preview=True, type="pil", interactive=False, ) generate_images = gr.Button(value="Generate Images", variant="primary") with gr.Accordion("Advance Settings", open=True): with gr.Row(): scheduler = gr.Dropdown( label="Scheduler", choices = [ "fm_euler" ], value="fm_euler", interactive=True ) with gr.Row(): for column in range(2): with gr.Column(): options = [ ("Height", "image_height", 64, 1024, 64, 1024, True), ("Width", "image_width", 64, 1024, 64, 1024, True), ("Num Images Per Prompt", "image_num_images_per_prompt", 1, 4, 1, 1, True), ("Num Inference Steps", "image_num_inference_steps", 1, 100, 1, 20, True), ("Clip Skip", "image_clip_skip", 0, 2, 1, 2, False), ("Guidance Scale", "image_guidance_scale", 0, 20, 0.5, 3.5, True), ("Seed", "image_seed", 0, 100000, 1, random.randint(0, 100000), True), ] for label, var_name, min_val, max_val, step, value, visible in options[column::2]: globals()[var_name] = gr.Slider(label=label, minimum=min_val, maximum=max_val, step=step, value=value, visible=visible, interactive=True) with gr.Row(): refiner = gr.Checkbox( label="Refiner 🧪", value=False, ) vae = gr.Checkbox( label="VAE", value=True, ) # Events # Base Options fast_generation.change(update_fast_generation, [fast_generation], [image_guidance_scale, image_num_inference_steps]) # Fast Generation # type: ignore # Lora Gallery lora_gallery.select(selected_lora_from_gallery, None, selected_lora) custom_lora.change(update_selected_lora, custom_lora, [custom_lora, selected_lora]) add_lora.click(add_to_enabled_loras, [selected_lora, enabled_loras], [selected_lora, custom_lora_info, enabled_loras]) enabled_loras.change(update_lora_sliders, enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, lora_remove_0, lora_remove_1, lora_remove_2, lora_remove_3, lora_remove_4, lora_remove_5]) # type: ignore for i in range(6): globals()[f"lora_remove_{i}"].click( lambda enabled_loras, index=i: remove_from_enabled_loras(enabled_loras, index), [enabled_loras], [enabled_loras] ) # Generate Image generate_images.click( generate_image, # type: ignore [ model, prompt, fast_generation, enabled_loras, lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5, # type: ignore img2img_image, inpaint_image, canny_image, pose_image, depth_image, # type: ignore img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, # type: ignore resize_mode, scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore image_num_inference_steps, image_guidance_scale, image_seed, # type: ignore refiner, vae ], [output_images] ) def sdxl_tab(): with gr.Tab("SDXL"): gr.Label("To be implemented")