app.py

import sentencepiece
import torch
import spaces
import gradio as gr
import os
from diffusers.pipelines.flux.pipeline_flux_controlnet_inpaint import (
    FluxControlNetInpaintPipeline,
)
from diffusers.models.controlnet_flux import FluxControlNetModel
from controlnet_aux import CannyDetector
import psutil

# login hf token
HF_TOKEN = os.getenv("HF_TOKEN")
# print(HF_TOKEN)
# from huggingface_hub import login
#
# login()

dtype = torch.float16
device = "cuda" if torch.cuda.is_available() else "cpu"

base_model = "black-forest-labs/FLUX.1-dev"
controlnet_model = "YishaoAI/flux-dev-controlnet-canny-kid-clothes"

controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=dtype)
pipe = FluxControlNetInpaintPipeline.from_pretrained(
    base_model, controlnet=controlnet, torch_dtype=dtype
).to(device)

pipe.enable_model_cpu_offload()

canny = CannyDetector()


def get_system_memory():
    memory = psutil.virtual_memory()
    memory_percent = memory.percent
    memory_used = memory.used / (1024.0**3)
    memory_total = memory.total / (1024.0**3)
    return {
        "percent": f"{memory_percent}%",
        "used": f"{memory_used:.3f}GB",
        "total": f"{memory_total:.3f}GB",
    }


@spaces.GPU(duration=160)
def inpaint(
    image,
    mask,
    prompt,
    strength,
    num_inference_steps,
    guidance_scale,
    controlnet_conditioning_scale,
):
    canny_image = canny(image)

    image_res = pipe(
        prompt,
        image=image,
        control_image=canny_image,
        controlnet_conditioning_scale=controlnet_conditioning_scale,
        mask_image=mask,
        strength=strength,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
    ).images[0]

    return image_res


with gr.Blocks() as demo:
    # gr.LoginButton()
    with gr.Row():
        with gr.Column():
            gr.Textbox(value="Hello Memory")
        with gr.Column():
            gr.JSON(get_system_memory, every=1)
    gr.Interface(
        fn=inpaint,
        inputs=[
            gr.Image(type="pil", label="Input Image"),
            gr.Image(type="pil", label="Mask Image"),
            gr.Textbox(label="Prompt"),
            gr.Slider(0, 1, value=0.95, label="Strength"),
            gr.Slider(1, 100, value=50, step=1, label="Number of Inference Steps"),
            gr.Slider(0, 20, value=5, label="Guidance Scale"),
            gr.Slider(0, 1, value=0.5, label="ControlNet Conditioning Scale"),
        ],
        outputs=gr.Image(type="pil", label="Output Image"),
        title="Flux Inpaint AI Model",
        description="Upload an image and a mask, then provide a prompt to generate an inpainted image.",
    )

demo.launch(debug=True)

# import gradio as gr
# import numpy as np
# import random
# # import spaces
# import torch
# from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
# from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
# # from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
#
# dtype = torch.bfloat16
# device = "cuda" if torch.cuda.is_available() else "cpu"
#
# taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
# good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-schnell", subfolder="vae", torch_dtype=dtype).to(device)
# pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype, vae=taef1).to(device)
# torch.cuda.empty_cache()
#
# MAX_SEED = np.iinfo(np.int32).max
# MAX_IMAGE_SIZE = 2048
#
# # pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
#
# # @spaces.GPU(duration=75)
# def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
#     if randomize_seed:
#         seed = random.randint(0, MAX_SEED)
#     generator = torch.Generator().manual_seed(seed)
#
#     for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
#             prompt=prompt,
#             guidance_scale=guidance_scale,
#             num_inference_steps=num_inference_steps,
#             width=width,
#             height=height,
#             generator=generator,
#             output_type="pil",
#             good_vae=good_vae,
#         ):
#             yield img, seed
#
# examples = [
#     "a tiny astronaut hatching from an egg on the moon",
#     "a cat holding a sign that says hello world",
#     "an anime illustration of a wiener schnitzel",
# ]
#
# css="""
# #col-container {
#     margin: 0 auto;
#     max-width: 520px;
# }
# """
#
# with gr.Blocks(css=css) as demo:
#
#     with gr.Column(elem_id="col-container"):
#         gr.Markdown(f"""# FLUX.1 [dev]
# 12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
# [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
#         """)
#
#         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)
#
#         result = gr.Image(label="Result", show_label=False)
#
#         with gr.Accordion("Advanced Settings", open=False):
#
#             seed = gr.Slider(
#                 label="Seed",
#                 minimum=0,
#                 maximum=MAX_SEED,
#                 step=1,
#                 value=0,
#             )
#
#             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=1024,
#                 )
#
#                 height = gr.Slider(
#                     label="Height",
#                     minimum=256,
#                     maximum=MAX_IMAGE_SIZE,
#                     step=32,
#                     value=1024,
#                 )
#
#             with gr.Row():
#
#                 guidance_scale = gr.Slider(
#                     label="Guidance Scale",
#                     minimum=1,
#                     maximum=15,
#                     step=0.1,
#                     value=3.5,
#                 )
#
#                 num_inference_steps = gr.Slider(
#                     label="Number of inference steps",
#                     minimum=1,
#                     maximum=50,
#                     step=1,
#                     value=28,
#                 )
#
#         gr.Examples(
#             examples = examples,
#             fn = infer,
#             inputs = [prompt],
#             outputs = [result, seed],
#             cache_examples="lazy"
#         )
#
#     gr.on(
#         triggers=[run_button.click, prompt.submit],
#         fn = infer,
#         inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
#         outputs = [result, seed]
#     )
#
# demo.launch()