app.py

from __future__ import annotations
import math
import random
import spaces
import gradio as gr
import numpy as np
import torch
from PIL import Image
from diffusers import DiffusionPipeline, StableDiffusionXLPipeline, EDMEulerScheduler, StableDiffusionXLInstructPix2PixPipeline, AutoencoderKL
from huggingface_hub import hf_hub_download
from huggingface_hub import InferenceClient

vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLPipeline.from_pretrained("fluently/Fluently-XL-Final", torch_dtype=torch.float16, vae=vae)
pipe.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
pipe.set_adapters("lora")
pipe.to("cuda")

refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
refiner.to("cuda")

help_text = """
To optimize image results:
- Adjust the **Image CFG weight** if the image isn't changing enough or is changing too much. Lower it to allow bigger changes, or raise it to preserve original details.
- Modify the **Text CFG weight** to influence how closely the edit follows text instructions. Increase it to adhere more to the text, or decrease it for subtler changes.
- Experiment with different **random seeds** and **CFG values** for varied outcomes.
- **Rephrase your instructions** for potentially better results.
- **Increase the number of steps** for enhanced edits.
"""

def set_timesteps_patched(self, num_inference_steps: int, device = None):
    self.num_inference_steps = num_inference_steps
    
    ramp = np.linspace(0, 1, self.num_inference_steps)
    sigmas = torch.linspace(math.log(self.config.sigma_min), math.log(self.config.sigma_max), len(ramp)).exp().flip(0)
    
    sigmas = (sigmas).to(dtype=torch.float32, device=device)
    self.timesteps = self.precondition_noise(sigmas)
    
    self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)])
    self._step_index = None
    self._begin_index = None
    self.sigmas = self.sigmas.to("cpu") 

# Image Editor
edit_file = hf_hub_download(repo_id="stabilityai/cosxl", filename="cosxl_edit.safetensors")
EDMEulerScheduler.set_timesteps = set_timesteps_patched
pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file( edit_file, num_in_channels=8, is_cosxl_edit=True, vae=vae, torch_dtype=torch.float16 )
pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
pipe_edit.to("cuda")

# Generator
@spaces.GPU(duration=30, queue=False)
def king(type ,
        input_image ,
        instruction: str ,
        steps: int = 8,
        randomize_seed: bool = False,
        seed: int = 25,
        text_cfg_scale: float = 7.3,
        image_cfg_scale: float = 1.7,
        width: int = 1024,
        height: int = 1024,
        guidance_scale: float = 6,
        use_resolution_binning: bool = True,
        progress=gr.Progress(track_tqdm=True),
    ):
    if type=="Image Editing" :
        if randomize_seed:
            seed = random.randint(0, 99999)
        text_cfg_scale = text_cfg_scale
        image_cfg_scale = image_cfg_scale
        input_image = input_image
        steps=steps
        generator = torch.manual_seed(seed)
        output_image = pipe_edit(
            instruction, image=input_image,
            guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale,
            num_inference_steps=steps, generator=generator, output_type="latent",
        ).images         

        refine = refiner(
            prompt=instruction,
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            image=output_image,
            generator=generator,
        ).images[0]  
        return seed, refine
    else :
        if randomize_seed:
            seed = random.randint(0, 99999)
        generator = torch.Generator().manual_seed(seed)
        image = pipe(
            prompt = instruction,
            guidance_scale = guidance_scale, 
            num_inference_steps = steps, 
            width = (width), 
            height = (height),
            generator = generator,
            output_type="latent",
        ).images         

        refine = refiner(
            prompt=instruction,
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            image=image,
            generator=generator,
        ).images[0]        
        return seed, refine

client = InferenceClient()
# Prompt classifier
def response(instruction, input_image=None ):
    if input_image is None:
        output="Image Generation"
    else:
        try:
            text = instruction
            labels = ["Image Editing", "Image Generation"]
            classification = client.zero_shot_classification(text, labels, multi_label=True)
            output = classification[0]
            output = str(output)
            if "Editing" in output:
                output = "Image Editing"
            else:
                output = "Image Generation"
        except error:
            if input_image is None:
                output="Image Generation"
            else:
                output="Image Editing"
    return output

css = '''
.gradio-container{max-width: 700px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

examples=[
        [
            "Image Generation",
            None,
            "A luxurious supercar with a unique design. The car should have a pearl white finish, and gold accents. 4k, realistic.",

        ],
        [
            "Image Editing",
            "./supercar.png",
            "make it red",

        ],
        [
            "Image Editing",
            "./red_car.png",
            "add some snow",

        ],
        [
            "Image Generation",
            None,
            "An alien grasping a sign board contain word 'ALIEN' with Neon Glow, neon, futuristic, neonpunk, neon lights",
        ],
        [
            "Image Generation",
            None,
            "Beautiful Eiffel Tower at Night",
        ],
    ]

with gr.Blocks(css=css) as demo:
    gr.Markdown("# Image Generator Pro")
    with gr.Row():
        instruction = gr.Textbox(lines=1, label="Instruction", interactive=True)
    with gr.Row():
        with gr.Column(scale=1):
            type = gr.Dropdown(["Image Generation","Image Editing"], label="Task", value="Image Generation",interactive=True)
        with gr.Column(scale=1):
            generate_button = gr.Button("Generate")

    with gr.Row():
        input_image = gr.Image(label="Image", type="pil", interactive=True)

    with gr.Row():
        text_cfg_scale = gr.Number(value=7.3, step=0.1, label="Text CFG", interactive=True)
        image_cfg_scale = gr.Number(value=1.7, step=0.1,label="Image CFG", interactive=True)
        guidance_scale = gr.Number(value=6.0, step=0.1, label="Image Generation Guidance Scale", interactive=True)
        steps = gr.Number(value=25, step=1, label="Steps", interactive=True)
        randomize_seed = gr.Radio(
                ["Fix Seed", "Randomize Seed"],
                value="Randomize Seed",
                type="index",
                show_label=False,
                interactive=True,
            )
        seed = gr.Number(value=1371, step=1, label="Seed", interactive=True)

    with gr.Row():
        width =  gr.Slider( label="Width", minimum=256, maximum=2048, step=64, value=1024)
        height =  gr.Slider( label="Height", minimum=256, maximum=2048, step=64, value=1024)

    gr.Examples(
        examples=examples,
        inputs=[type,input_image, instruction],
        fn=king,
        outputs=[input_image],
        cache_examples=False,
    )

    gr.Markdown(help_text)

    instruction.change(fn=response, inputs=[instruction,input_image], outputs=type, queue=False)

    input_image.upload(fn=response, inputs=[instruction,input_image], outputs=type, queue=False)
    
    gr.on(triggers=[
            generate_button.click,
            instruction.submit
        ],
            fn=king,
            inputs=[type,
                input_image,
                instruction,
                steps,
                randomize_seed,
                seed,
                text_cfg_scale,
                image_cfg_scale,
                width,
                height,
                guidance_scale,
            ],
            outputs=[seed, input_image],
        )

demo.queue(max_size=99999).launch()