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

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

dtype = torch.bfloat16
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()

@spaces.GPU(duration=75)
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


iface = 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.",
    )

iface.launch()

    # 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()