import spaces import gradio as gr from huggingface_hub import ModelCard from modules.helpers.common_helpers import ControlNetReq, BaseReq, BaseImg2ImgReq, BaseInpaintReq from modules.helpers.sdxl_helpers import gen_img from config import sdxl_loras loras = sdxl_loras # Event functions def update_fast_generation(fast_generation): if fast_generation: return ( gr.update( value=0.0 ), gr.update( value=8 ) ) else: return ( gr.update( value=7.0 ), gr.update( value=20 ) ) def add_to_enabled_loras(selected_lora, enabled_loras): lora_data = loras try: selected_lora = int(selected_lora) if 0 <= selected_lora: # is the index of the lora in the gallery lora_info = lora_data[selected_lora] enabled_loras.append({ "repo_id": lora_info["repo"], "trigger_word": lora_info["trigger_word"] }) except ValueError: link = selected_lora.split("/") if len(link) == 2: model_card = ModelCard.load(selected_lora) trigger_word = model_card.data.get("instance_prompt", "") enabled_loras.append({ "repo_id": selected_lora, "trigger_word": trigger_word }) return ( gr.update( # selected_lora value="" ), gr.update( # custom_lora_info value="", visible=False ), gr.update( # enabled_loras value=enabled_loras ) ) @spaces.GPU(duration=75) def generate_image( model, prompt, negative_prompt, fast_generation, enabled_loras, enabled_embeddings, # type: ignore 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, scribble_image, # type: ignore img2img_strength, inpaint_strength, canny_strength, pose_strength, depth_strength, scribble_strength, # type: ignore resize_mode, scheduler, image_height, image_width, image_num_images_per_prompt, # type: ignore image_num_inference_steps, image_clip_skip, image_guidance_scale, image_seed, # type: ignore refiner, vae ): try: base_args = { "model": model, "prompt": prompt, "negative_prompt": negative_prompt, "fast_generation": fast_generation, "loras": None, "embeddings": None, "resize_mode": resize_mode, "scheduler": scheduler, "height": image_height, "width": image_width, "num_images_per_prompt": image_num_images_per_prompt, "num_inference_steps": image_num_inference_steps, "clip_skip": image_clip_skip, "guidance_scale": image_guidance_scale, "seed": image_seed, "refiner": refiner, "vae": vae, "controlnet_config": None, } base_args = BaseReq(**base_args) if len(enabled_loras) > 0: base_args.loras = [] for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]): if enabled_lora["repo_id"]: base_args.loras.append({ "repo_id": enabled_lora["repo_id"], "weight": slider }) if len(enabled_embeddings) > 0: base_args.embeddings = enabled_embeddings image = None mask_image = None strength = None if img2img_image: image = img2img_image strength = float(img2img_strength) base_args = BaseImg2ImgReq( **base_args.__dict__, image=image, strength=strength ) elif inpaint_image: image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None mask_image = inpaint_image['layers'][0] if image else None strength = float(inpaint_strength) if image and mask_image: base_args = BaseInpaintReq( **base_args.__dict__, image=image, mask_image=mask_image, strength=strength ) elif any([canny_image, pose_image, depth_image]): base_args.controlnet_config = ControlNetReq( controlnets=[], control_images=[], controlnet_conditioning_scale=[] ) if canny_image: base_args.controlnet_config.controlnets.append("canny") base_args.controlnet_config.control_images.append(canny_image) base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength)) if pose_image: base_args.controlnet_config.controlnets.append("pose") base_args.controlnet_config.control_images.append(pose_image) base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength)) if depth_image: base_args.controlnet_config.controlnets.append("depth") base_args.controlnet_config.control_images.append(depth_image) base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength)) if scribble_image: base_args.controlnet_config.controlnets.append("scribble") base_args.controlnet_config.control_images.append(scribble_image) base_args.controlnet_config.controlnet_conditioning_scale.append(float(scribble_strength)) else: base_args = BaseReq(**base_args.__dict__) return gr.update( value=gen_img(base_args), interactive=True ) except Exception as e: raise gr.Error(f"Error: {e}") from e