app.py

import gradio as gr
import numpy as np
import torch
from diffusers.utils import load_image, make_image_grid
from diffusers import (
    StableDiffusionPipeline,
    StableDiffusionControlNetPipeline,
    ControlNetModel
)
from peft import PeftModel, LoraConfig
from controlnet_aux import HEDdetector
from PIL import Image
import cv2 as cv
import os


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
IP_ADAPTER = 'h94/IP-Adapter'
IP_ADAPTER_WEIGHT_NAME = "ip-adapter-plus_sd15.bin"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id_default = "CompVis/stable-diffusion-v1-4"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

hed = None
dict_controlnet = {
    "edge_detection": "lllyasviel/sd-controlnet-canny", 
    # "pose_estimation": "lllyasviel/sd-controlnet-openpose",
    # "depth_map": "lllyasviel/sd-controlnet-depth", 
    "scribble": "lllyasviel/sd-controlnet-scribble", 
    # "MLSD": "lllyasviel/sd-controlnet-mlsd"
}

controlnet = ControlNetModel.from_pretrained(
    dict_controlnet["edge_detection"],
    cache_dir="./models_cache",
    torch_dtype=torch_dtype,
)


def get_lora_sd_pipeline(
    ckpt_dir='./lora_logos', 
    base_model_name_or_path=None, 
    dtype=torch.float16, 
    adapter_name="default",
    controlnet=None
    ):

    unet_sub_dir = os.path.join(ckpt_dir, "unet")
    text_encoder_sub_dir = os.path.join(ckpt_dir, "text_encoder")
    
    if os.path.exists(text_encoder_sub_dir) and base_model_name_or_path is None:
        config = LoraConfig.from_pretrained(text_encoder_sub_dir)
        base_model_name_or_path = config.base_model_name_or_path
    
    if base_model_name_or_path is None:
        raise ValueError("Please specify the base model name or path")
    
    pipe = StableDiffusionControlNetPipeline.from_pretrained(
        base_model_name_or_path, 
        torch_dtype=dtype,
        controlnet=controlnet,
    )

    before_params = pipe.unet.parameters()
    pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)
    pipe.unet.set_adapter(adapter_name)
    after_params = pipe.unet.parameters()
    print("Parameters changed:", any(torch.any(b != a) for b, a in zip(before_params, after_params)))
    
    if os.path.exists(text_encoder_sub_dir):
        pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)
    
    if dtype in (torch.float16, torch.bfloat16):
        pipe.unet.half()
        pipe.text_encoder.half()

    return pipe

def process_prompt(prompt, tokenizer, text_encoder, max_length=77):
    tokens = tokenizer(prompt, truncation=False, return_tensors="pt")["input_ids"]
    chunks = [tokens[:, i:i + max_length] for i in range(0, tokens.shape[1], max_length)]
    
    with torch.no_grad():
        embeds = [text_encoder(chunk.to(text_encoder.device))[0] for chunk in chunks]
    
    return torch.cat(embeds, dim=1)

def align_embeddings(prompt_embeds, negative_prompt_embeds):
    max_length = max(prompt_embeds.shape[1], negative_prompt_embeds.shape[1])
    return torch.nn.functional.pad(prompt_embeds, (0, 0, 0, max_length - prompt_embeds.shape[1])), \
           torch.nn.functional.pad(negative_prompt_embeds, (0, 0, 0, max_length - negative_prompt_embeds.shape[1]))

def map_edge_detection(image_path: str) -> Image:
    source_img = load_image(image_path).convert('RGB')
    edges = cv.Canny(np.array(source_img), 80, 160)
    edges = np.repeat(edges[:, :, None], 3, axis=2)
    final_image = Image.fromarray(edges)
    return final_image

def map_scribble(image_path: str) -> Image:
    global hed
    if not hed:
        hed = HEDdetector.from_pretrained('lllyasviel/Annotators')
    
    image = load_image(image_path).convert('RGB')
    scribble_image = hed(image)
    image_np = np.array(scribble_image)
    image_np = cv.medianBlur(image_np, 3)
    image = cv.convertScaleAbs(image_np, alpha=1.5, beta=0)
    final_image = Image.fromarray(image)
    return final_image



pipe = get_lora_sd_pipeline(
    ckpt_dir='./lora_logos', 
    base_model_name_or_path=model_id_default, 
    dtype=torch_dtype,
    controlnet=controlnet
).to(device)



def infer(
    prompt, 
    negative_prompt, 
    width=512, 
    height=512, 
    num_inference_steps=20, 
    model_id='CompVis/stable-diffusion-v1-4', 
    seed=42, 
    guidance_scale=7.0, 
    lora_scale=0.5,
    cn_enable=False,
    cn_strength=0.0,
    cn_mode='edge_detection',
    cn_image=None,
    ip_enable=False,
    ip_scale=0.5,
    ip_image=None,
    progress=gr.Progress(track_tqdm=True)
    ):
    
    generator = torch.Generator(device).manual_seed(seed)
    
    global pipe
    global controlnet
    
    controlnet_changed = False

    if cn_enable:
        if dict_controlnet[cn_mode] != pipe.controlnet._name_or_path:
            controlnet = ControlNetModel.from_pretrained(
                dict_controlnet[cn_mode], 
                cache_dir="./models_cache", 
                torch_dtype=torch_dtype
            )
            controlnet_changed = True
    else:
        cn_strength = 0.0  # отключаем контролнет принудительно

    if model_id != pipe._name_or_path:
        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            model_id, 
            torch_dtype=torch_dtype,
            controlnet=controlnet,
            controlnet_conditioning_scale=cn_strength,
        ).to(device)
    elif (model_id == pipe._name_or_path) and controlnet_changed:
        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            model_id, 
            torch_dtype=torch_dtype,
            controlnet=controlnet,
            controlnet_conditioning_scale=cn_strength,
        ).to(device)
        print(f"LoRA adapter loaded: {pipe.unet.active_adapters}")
        print(f"LoRA scale applied: {lora_scale}")
        pipe.fuse_lora(lora_scale=lora_scale)
    elif (model_id == pipe._name_or_path) and not controlnet_changed:
        print(f"LoRA adapter loaded: {pipe.unet.active_adapters}")
        print(f"LoRA scale applied: {lora_scale}")
        pipe.fuse_lora(lora_scale=lora_scale)

    prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
    negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
    prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
    
    params = {
        'prompt_embeds': prompt_embeds,
        'negative_prompt_embeds': negative_prompt_embeds,
        'guidance_scale': guidance_scale,
        'num_inference_steps': num_inference_steps,
        'width': width,
        'height': height,
        'generator': generator,
    }

    if cn_enable:
        params['controlnet_conditioning_scale'] = cn_strength
        if cn_mode == 'edge_detection':
            control_image = map_edge_detection(cn_image)
            print(type(control_image))
        elif cn_mode == 'scribble':
            control_image = map_scribble(cn_image)
        params['control_image'] = control_image

    if ip_enable:
        pipe.load_ip_adapter(
            IP_ADAPTER,
            subfolder="models",
            weight_name=IP_ADAPTER_WEIGHT_NAME,
        )
        params['ip_adapter_image'] = load_image(ip_image).convert('RGB')
        pipe.ip_scale(0.6)
    
    return pipe(**params).images[0]

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(" # DEMO Text-to-Image")
        
        with gr.Row():
            model_id = gr.Textbox(
                label="Model ID",
                max_lines=1,
                placeholder="Enter model id like 'CompVis/stable-diffusion-v1-4'",
                value=model_id_default
            )

        prompt = gr.Textbox(
            label="Prompt",
            max_lines=1,
            placeholder="Enter your prompt",
        )

        negative_prompt = gr.Textbox(
            label="Negative prompt",
            max_lines=1,
            placeholder="Enter a negative prompt",
        )

        with gr.Row():
            seed = gr.Number(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance scale",
                minimum=0.0,
                maximum=10.0,
                step=0.1,
                value=7.0,
            )

        with gr.Row():
            lora_scale = gr.Slider(
                label="LoRA scale",
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.5,
            )

        with gr.Row():
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=50,
                step=1,
                value=20,
            )

        # Секция Control Net
        cn_enable = gr.Checkbox(label="Enable ControlNet")    
        with gr.Column(visible=False) as cn_options:
            with gr.Row():
                cn_strength = gr.Slider(0, 2, value=0.8, step=0.1, label="Control strength", interactive=True)
                cn_mode = gr.Dropdown(
                    choices=["edge_detection", "scribble"],
                    value="edge_detection",
                    label="Work regime",
                    interactive=True,
                )
            cn_image = gr.Image(type="filepath", label="Control image")

        cn_enable.change(
            lambda x: gr.update(visible=x),
            inputs=cn_enable,
            outputs=cn_options
        )
        
        # Секция IP-Adapter
        ip_enable = gr.Checkbox(label="Enable IP-Adapter")
        with gr.Column(visible=False) as ip_options:
            ip_scale = gr.Slider(0, 1, value=0.5, step=0.1, label="IP-adapter scale", interactive=True)
            ip_image = gr.Image(type="filepath", label="IP-adapter image", interactive=True)

        ip_enable.change(
            lambda x: gr.update(visible=x),
            inputs=ip_enable,
            outputs=ip_options
        )

        with gr.Accordion("Optional Settings", open=False):
            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            
            with gr.Row():
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )

        run_button = gr.Button("Run", scale=1, variant="primary")
        result = gr.Image(label="Result", show_label=False)
    
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            width,
            height,
            num_inference_steps,
            model_id,
            seed,
            guidance_scale,
            lora_scale,
            cn_enable,
            cn_strength,
            cn_mode,
            cn_image,
            ip_enable,
            ip_scale,
            ip_image
        ],
        outputs=[result],
    )

if __name__ == "__main__":
    demo.launch()