Function generate_image

apps/gradio/entity_level_control.py:281–324

(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress())

                        triggers=run_button.click
                    )
                    def generate_image(model_type, model_path, prompt, negative_prompt, cfg_scale, embedded_guidance, num_inference_steps, height, width, return_with_mask, seed, input_image, background_weight, random_mask_dir, *args, progress=gr.Progress()):
                        _, pipe = load_model(model_type, model_path)
                        input_params = {
                            "prompt": prompt,
                            "negative_prompt": negative_prompt,
                            "cfg_scale": cfg_scale,
                            "num_inference_steps": num_inference_steps,
                            "height": height,
                            "width": width,
                            "progress_bar_cmd": progress.tqdm,
                        }
                        if isinstance(pipe, FluxImagePipeline):
                            input_params["embedded_guidance"] = embedded_guidance
                        if input_image is not None:
                            input_params["input_image"] = input_image.resize((width, height)).convert("RGB")
                            input_params["enable_eligen_inpaint"] = True

                        local_prompt_list, canvas_list = (
                            args[0 * config["max_num_painter_layers"]: 1 * config["max_num_painter_layers"]],
                            args[1 * config["max_num_painter_layers"]: 2 * config["max_num_painter_layers"]],
                        )
                        local_prompts, masks = [], []
                        for local_prompt, canvas in zip(local_prompt_list, canvas_list):
                            if isinstance(local_prompt, str) and len(local_prompt) > 0:
                                local_prompts.append(local_prompt)
                                masks.append(Image.fromarray(canvas["layers"][0][:, :, -1]).convert("RGB"))
                        entity_masks = None if len(masks) == 0 else masks
                        entity_prompts = None if len(local_prompts) == 0 else local_prompts
                        input_params.update({
                            "eligen_entity_prompts": entity_prompts,
                            "eligen_entity_masks": entity_masks,
                        })
                        torch.manual_seed(seed)
                        # save_mask_prompts(masks, local_prompts, prompt, seed, random_mask_dir)
                        image = pipe(**input_params)
                        masks = [mask.resize(image.size) for mask in masks]
                        image_with_mask = visualize_masks(image, masks, local_prompts)

                        real_output = gr.State(image)
                        mask_out = gr.State(image_with_mask)

                        if return_with_mask:
                            return image_with_mask, real_output, mask_out
                        return image, real_output, mask_out

                    @gr.on(inputs=[input_image] + canvas_list, outputs=canvas_list, triggers=send_input_to_painter.click)
                    def send_input_to_painter_background(input_image, *canvas_list):