app.py

import gradio as gr
import numpy as np
import random
from typing import Optional
from rembg import remove

# import spaces #[uncomment to use ZeroGPU]
from diffusers import StableDiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
import torch


device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

DEFAULT_SEED = 42
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
DEFAULT_WIDTH = 512
DEFAULT_HEIGHT = 512
DEFAULT_GS = 7.5
DEFAULT_LS = 1.0
DEFAULT_NUM_INF_STEPS = 50
DEFAULT_CN_COND_SCALE = 1.0
DEFAULT_IPA_SCALE = 0.5


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(lora_model_id: Optional[str] = "osmr/stable-diffusion-v1-4-lora-iv-ghibli",
          prompt: str = "",
          negative_prompt: str = "",
          seed: Optional[int] = DEFAULT_SEED,
          randomize_seed: bool = True,
          width: int = DEFAULT_WIDTH,
          height: int = DEFAULT_HEIGHT,
          guidance_scale: Optional[float] = DEFAULT_GS,
          lora_scale: Optional[float] = DEFAULT_LS,
          num_inference_steps: Optional[int] = DEFAULT_NUM_INF_STEPS,

          controlnet_type: str = "Edge-Detection",
          controlnet_cond_scale: float = DEFAULT_CN_COND_SCALE,
          controlnet_image: object = None,
          ipadapter_scale: float = DEFAULT_IPA_SCALE,
          ipadapter_image: object = None,

          do_remove_bg: bool = False,

          progress = gr.Progress(track_tqdm=True)):

    use_lora = (lora_model_id in [
        "osmr/stable-diffusion-v1-4-lora-iv-ghibli",
        "osmr/stable-diffusion-v1-4-lora-db-ghibli",
        "osmr/stable-diffusion-v1-5-lora-iv-ghibli",
        "osmr/stable-diffusion-v1-5-lora-db-ghibli",
    ])
    if not use_lora:
        model_id = lora_model_id
        lora_model_id = None
    else:
        if lora_model_id == "osmr/stable-diffusion-v1-4-lora-iv-ghibli":
            model_id = "CompVis/stable-diffusion-v1-4"
        elif lora_model_id == "osmr/stable-diffusion-v1-4-lora-db-ghibli":
            model_id = "CompVis/stable-diffusion-v1-4"
        elif lora_model_id == "osmr/stable-diffusion-v1-5-lora-iv-ghibli":
            model_id = "runwayml/stable-diffusion-v1-5"
        elif lora_model_id == "osmr/stable-diffusion-v1-5-lora-db-ghibli":
            model_id = "runwayml/stable-diffusion-v1-5"
        else:
            model_id = lora_model_id
            lora_model_id = None

    sd_version = "1.5" if (model_id == "runwayml/stable-diffusion-v1-5") else "1.4"

    use_controlnet = (controlnet_image is not None)
    use_ipadapter = (ipadapter_image is not None)

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    if use_controlnet:
        if sd_version == "1.4":
            if controlnet_type == "Edge-Detection":
                controlnet_id = "lllyasviel/sd-controlnet-canny"
            else:
                controlnet_id = "lllyasviel/sd-controlnet-openpose"
        else:
            if controlnet_type == "Edge-Detection":
                controlnet_id = "lllyasviel/control_v11p_sd15_canny"
            else:
                controlnet_id = "lllyasviel/control_v11p_sd15_openpose"

        controlnet = ControlNetModel.from_pretrained(
            pretrained_model_name_or_path=controlnet_id,
            torch_dtype=torch_dtype)

        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            pretrained_model_name_or_path=model_id,
            controlnet=controlnet,
            torch_dtype=torch_dtype)
    else:
        pipe = StableDiffusionPipeline.from_pretrained(
            pretrained_model_name_or_path=model_id,
            torch_dtype=torch_dtype)

    if use_ipadapter:
        pipe.load_ip_adapter(
            "h94/IP-Adapter",
            subfolder="models",
            weight_name="ip-adapter_sd15.bin")
        pipe.set_ip_adapter_scale(ipadapter_scale)

    if use_lora:
        pipe.load_lora_weights(lora_model_id)
        cross_attention_kwargs = {"scale": lora_scale}
    else:
        cross_attention_kwargs = None

    pipe = pipe.to(device)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
        cross_attention_kwargs=cross_attention_kwargs,
        image=controlnet_image,
        controlnet_conditioning_scale=(float(controlnet_cond_scale) if use_controlnet else None),
        ip_adapter_image=ipadapter_image
    ).images[0]

    if do_remove_bg:
        image = remove(image)

    return image, seed


examples = [
    "GBL, a man and a woman sitting at a table with glasses of wine in front of them",
    "a man and a woman sitting at a table with glasses of wine in front of them",
    "GBL, a man sitting at a desk in a library with a book open in front of him",
    "GBL, a cartoon woman is standing in front of a wall",
]

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(" # Ghibli LoRA generation")

        with gr.Row():
            lora_model_id = gr.Dropdown(
                choices=[
                    "osmr/stable-diffusion-v1-4-lora-iv-ghibli",
                    "osmr/stable-diffusion-v1-4-lora-db-ghibli",
                    "osmr/stable-diffusion-v1-5-lora-iv-ghibli",
                    "osmr/stable-diffusion-v1-5-lora-db-ghibli",
                    "CompVis/stable-diffusion-v1-4",
                    "runwayml/stable-diffusion-v1-5"],
                multiselect=False,
                allow_custom_value=True,
                label="Model",
            )
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                value="GBL, a man and a woman sitting at a table with glasses of wine in front of them",
                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,
                value="low quality, deformed, ugly, bad art, poorly drawn, bad anatomy, low detail, unrealistic",
                placeholder="Enter a negative prompt",
                visible=True,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=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=DEFAULT_WIDTH,
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=DEFAULT_HEIGHT,
                )

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

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

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

            do_remove_bg = gr.Checkbox(label="Remove background", value=False)

        with gr.Accordion("ControlNet Settings", open=False):
            controlnet_type = gr.Dropdown(
                choices=[
                    "Edge-Detection",
                    "Pose-Estimation"],
                interactive=True,
                label="ControlNet Type",
            )

            controlnet_cond_scale = gr.Slider(
                label="ControlNet Conditioning Scale",
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=DEFAULT_CN_COND_SCALE
            )

            controlnet_image = gr.Image(
                label="Control Image",
                type="pil",
                show_label=True)

        with gr.Accordion("IP-adapter Settings", open=False):
            ipadapter_scale = gr.Slider(
                label="IP-adapter Scale",
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=DEFAULT_IPA_SCALE
            )

            ipadapter_image = gr.Image(
                label="IP-adapter Image",
                type="pil",
                show_label=True)

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            lora_model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            lora_scale,
            num_inference_steps,
            controlnet_type,
            controlnet_cond_scale,
            controlnet_image,
            ipadapter_scale,
            ipadapter_image,
            do_remove_bg,
        ],
        outputs=[result, seed],
    )

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