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app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+from PIL import Image
+
+from diffusers import (
+    DiffusionPipeline,
+    StableDiffusionControlNetPipeline,
+    ControlNetModel
+)
+from peft import PeftModel
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+LORA_MODEL = "akaUNik/hw5-homm3-lora-15"
+LORA_BASE_MODEL = "runwayml/stable-diffusion-v1-5"
+
+# Model list including LoRA model
+MODEL_LIST = [
+    "runwayml/stable-diffusion-v1-5",
+    "stabilityai/sdxl-turbo",
+    "stabilityai/stable-diffusion-2-1",
+    LORA_MODEL,  # LoRA model option
+]
+
+# ControlNet modes list with aliases
+CONTROLNET_MODES = {
+    "Canny Edge Detection": "lllyasviel/control_v11p_sd15_canny",
+    "Pixel to Pixel": "lllyasviel/control_v11e_sd15_ip2p",
+    "Inpainting": "lllyasviel/control_v11p_sd15_inpaint",
+    "Multi-Level Line Segments": "lllyasviel/control_v11p_sd15_mlsd",
+    "Depth Estimation": "lllyasviel/control_v11f1p_sd15_depth",
+    "Surface Normal Estimation": "lllyasviel/control_v11p_sd15_normalbae",
+    "Image Segmentation": "lllyasviel/control_v11p_sd15_seg",
+    "Line Art Generation": "lllyasviel/control_v11p_sd15_lineart",
+    "Anime Line Art": "lllyasviel/control_v11p_sd15_lineart_anime",
+    "Human Pose Estimation": "lllyasviel/control_v11p_sd15_openpose",
+    "Scribble-Based Generation": "lllyasviel/control_v11p_sd15_scribble",
+    "Soft Edge Generation": "lllyasviel/control_v11p_sd15_softedge",
+    "Image Shuffling": "lllyasviel/control_v11e_sd15_shuffle",
+    "Image Tiling": "lllyasviel/control_v11f1e_sd15_tile",
+}
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+# Cache to avoid re-initializing pipelines repeatedly
+model_cache = {}
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 512
+
+def infer(
+    model_id,
+    prompt,
+    negative_prompt,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    guidance_scale,
+    num_inference_steps,
+    lora_scale,
+    controlnet_enable,
+    controlnet_mode,
+    controlnet_strength,
+    controlnet_image,
+    ip_adapter_enable,
+    ip_adapter_scale,
+    ip_adapter_image,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    # Cache
+    # if (model_id, controlnet_enable, controlnet_image, controlnet_mode) in model_cache:
+      # pipe = model_cache[(model_id, controlnet_enable, controlnet_image, controlnet_mode)]
+    # else:
+
+    pipe = None
+    if controlnet_enable and controlnet_image:
+        controlnet_model = ControlNetModel.from_pretrained(
+            CONTROLNET_MODES.get(controlnet_mode),
+            torch_dtype=torch_dtype
+        )
+        if model_id == LORA_MODEL:
+            pipe = StableDiffusionControlNetPipeline.from_pretrained(
+                LORA_BASE_MODEL,
+                controlnet=controlnet_model,
+                torch_dtype=torch_dtype
+            )
+        else:
+            pipe = StableDiffusionControlNetPipeline.from_pretrained(
+                model_id,
+                controlnet=controlnet_model,
+                torch_dtype=torch_dtype
+            )
+    else:
+        if model_id == LORA_MODEL:
+
+          # Use the specified base model for your LoRA adapter.
+          pipe = DiffusionPipeline.from_pretrained(
+              LORA_BASE_MODEL,
+              torch_dtype=torch_dtype
+          )
+
+          # Load the LoRA weights
+          pipe.unet = PeftModel.from_pretrained(
+              pipe.unet,
+              model_id,
+              subfolder="unet",
+              torch_dtype=torch_dtype
+          )
+          pipe.text_encoder = PeftModel.from_pretrained(
+              pipe.text_encoder,
+              model_id,
+              subfolder="text_encoder",
+              torch_dtype=torch_dtype
+          )
+        else:
+            pipe = DiffusionPipeline.from_pretrained(
+                model_id,
+                torch_dtype=torch_dtype
+            )
+
+    if ip_adapter_enable:
+      pipe.load_ip_adapter(
+          "h94/IP-Adapter",
+          subfolder="models",
+          weight_name="ip-adapter-plus_sd15.bin"
+      )
+      pipe.set_ip_adapter_scale(ip_adapter_scale)
+
+    pipe.safety_checker = None
+    pipe.to(device)
+    # model_cache[(model_id, controlnet_enable, controlnet_image, controlnet_mode)] = pipe
+
+    image = pipe(
+      prompt=prompt,
+      image=controlnet_image if controlnet_enable else None,
+      negative_prompt=negative_prompt,
+      guidance_scale=guidance_scale,
+      num_inference_steps=num_inference_steps,
+      width=width,
+      height=height,
+      generator=generator,
+      cross_attention_kwargs={"scale": lora_scale},
+      controlnet_conditioning_scale=controlnet_strength,
+      ip_adapter_image=ip_adapter_image if ip_adapter_enable else None
+    ).images[0]
+
+    return image, seed
+
+# @title Gradio
+examples = [
+    "homm3_spell_icon midivial sticker of a cartoon character of a man in a lab coat and glasses, old lady screaming and laughing",
+    "homm3_spell_icon midivial sticker of a cartoon man with a mustache and a hat on, portrait bender from futurama, telegram sticker",
+    "homm3_spell_icon midivial sticker of a cartoon character with a gun in his hand",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # Text-to-Image Gradio Template")
+
+        with gr.Row():
+            # Dropdown to select the model from Hugging Face
+            model_id = gr.Dropdown(
+                label="Model",
+                choices=MODEL_LIST,
+                value=MODEL_LIST[0],  # Default model
+            )
+
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0, variant="primary")
+
+        result = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            negative_prompt = gr.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+            )
+
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,  # Default seed
+            )
+
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+            with gr.Row():
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.5,
+                    value=7.0,
+                )
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=20,
+                )
+
+            # New slider for LoRA scale.
+            lora_scale = gr.Slider(
+                label="LoRA Scale",
+                minimum=0.0,
+                maximum=2.0,
+                step=0.1,
+                value=1.0,
+                info="Adjust the influence of the LoRA weights",
+            )
+
+        # --- ControlNet Settings ---
+        with gr.Accordion("ControlNet Settings", open=False):
+            controlnet_enable = gr.Checkbox(
+                label="Enable ControlNet",
+                value=False
+            )
+            with gr.Group(visible=False) as controlnet_group:
+                controlnet_mode = gr.Dropdown(
+                    label="ControlNet Mode",
+                    choices=list(CONTROLNET_MODES.keys()),
+                    value=list(CONTROLNET_MODES.keys())[0],
+                )
+                controlnet_strength = gr.Slider(
+                    label="ControlNet Conditioning Scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.7,
+                )
+                controlnet_image = gr.Image(
+                    label="ControlNet Image",
+                    type="pil"
+                )
+
+            def show_controlnet_options(enable):
+                return {controlnet_group: gr.update(visible=enable)}
+
+            controlnet_enable.change(
+                fn=show_controlnet_options,
+                inputs=controlnet_enable,
+                outputs=controlnet_group,
+            )
+
+        # --- IP-adapter Settings ---
+        with gr.Accordion("IP-adapter Settings", open=False):
+            ip_adapter_enable = gr.Checkbox(
+                label="Enable IP-adapter",
+                value=False
+            )
+
+            with gr.Group(visible=False) as ip_adapter_group:
+                ip_adapter_scale = gr.Slider(
+                    label="IP-adapter Scale",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.1,
+                    value=1.0
+                )
+                ip_adapter_image = gr.Image(
+                    label="IP-adapter Image",
+                    type="pil"
+                )
+
+            # Show/hide IP-adapter parameters when checkbox is toggled
+            def show_ip_adapter_options(enable):
+                return {ip_adapter_group: gr.update(visible=enable)}
+
+            ip_adapter_enable.change(
+                fn=show_ip_adapter_options,
+                inputs=ip_adapter_enable,
+                outputs=ip_adapter_group,
+            )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            model_id,
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            lora_scale,
+            controlnet_enable,
+            controlnet_mode,
+            controlnet_strength,
+            controlnet_image,
+            ip_adapter_enable,
+            ip_adapter_scale,
+            ip_adapter_image,
+        ],
+        outputs=[result, seed],
+    )
+
+# @title Run
+
+if __name__ == "__main__":
+    demo.launch(debug=True) # show errors in colab notebook

ДЗ_6.md

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+# Добавляем больше опций для контроля генерации стикеров.
+
+#### Цель:
+
+Добавить в пользовательский интерфейс Gradio опции, позволяющие использовать ControlNet и IP-adapter для управления генерацией стикеров, а также обеспечить возможность загрузки изображений, необходимых для их работы.
+
+#### Задача:
+
+В ваш интерфейс на HuggingFace добавьте новые элементы управления:
+- Чекбокс для включения/отключения использования ControlNet. При активации ControlNet отобразите дополнительные опции:
+    - Слайдер для настройки интенсивности влияния (`control_strength`).
+    - Выпадающий список для выбора режима работы ControlNet (например, `edge_detection`, `pose_estimation` другие из [репозитория](https://github.com/lllyasviel/ControlNet)).
+    - Окно для загрузки изображений, используемых для настройки ControlNet.
+- Чекбокс для включения/отключения IP-adapter. При активации IP-adapter добавьте возможность регулировки его параметров:
+    - Слайдер для настройки `ip_adapter_scale`.
+    - Окно для загрузки изображений для IP-adapter.
+
+Проверьте работу интерфейса, запустив тестовые генерации с разными комбинациями настроек, чтобы убедиться, что изменения отражаются корректно. Отдельно проверьте, что можно включать и отключать ControlNet и IP-adapter как по отдельности, так и вместе.
+
+
+#### Как сдать домашнее задание
+Для сдачи домашнего задания загрузите в ваш репозиторий код вашего Space из HuggingFace и публичную ссылку на него.