app.py

import os
import gc
import gradio as gr
import numpy as np
import spaces
import torch
import random
from PIL import Image
from typing import Iterable
from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes

colors.orange_red = colors.Color(
    name="orange_red",
    c50="#FFF0E5",
    c100="#FFE0CC",
    c200="#FFC299",
    c300="#FFA366",
    c400="#FF8533",
    c500="#FF4500",
    c600="#E63E00",
    c700="#CC3700",
    c800="#B33000",
    c900="#992900",
    c950="#802200",
)

class OrangeRedTheme(Soft):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.gray,
        secondary_hue: colors.Color | str = colors.orange_red,
        neutral_hue: colors.Color | str = colors.slate,
        text_size: sizes.Size | str = sizes.text_lg,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        super().set(
            background_fill_primary="*primary_50",
            background_fill_primary_dark="*primary_900",
            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
            button_primary_text_color="white",
            button_primary_text_color_hover="white",
            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)",
            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)",
            button_secondary_text_color="black",
            button_secondary_text_color_hover="white",
            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
            slider_color="*secondary_500",
            slider_color_dark="*secondary_600",
            block_title_text_weight="600",
            block_border_width="3px",
            block_shadow="*shadow_drop_lg",
            button_primary_shadow="*shadow_drop_lg",
            button_large_padding="11px",
            color_accent_soft="*primary_100",
            block_label_background_fill="*primary_200",
        )

orange_red_theme = OrangeRedTheme()

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

print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
print("torch.__version__ =", torch.__version__)
print("Using device:", device)

from diffusers import FlowMatchEulerDiscreteScheduler
from qwenimage.pipeline_qwenimage_edit_plus import QwenImageEditPlusPipeline
from qwenimage.transformer_qwenimage import QwenImageTransformer2DModel
from qwenimage.qwen_fa3_processor import QwenDoubleStreamAttnProcessorFA3

dtype = torch.bfloat16

# FIXED TYPO HERE (from_pretrained instead of frompretrained)
pipe = QwenImageEditPlusPipeline.from_pretrained(
    "FireRedTeam/FireRed-Image-Edit-1.0",
    transformer=QwenImageTransformer2DModel.from_pretrained(
        "prithivMLmods/Qwen-Image-Edit-Rapid-AIO-V19",
        torch_dtype=dtype,
        device_map='cuda'
    ),
    torch_dtype=dtype
).to(device)

try:
    pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3())
    print("Flash Attention 3 Processor set successfully.")
except Exception as e:
    print(f"Warning: Could not set FA3 processor: {e}")

MAX_SEED = np.iinfo(np.int32).max

def update_dimensions_on_upload(image):
    if image is None:
        return 1024, 1024

    original_width, original_height = image.size

    if original_width > original_height:
        new_width = 1024
        aspect_ratio = original_height / original_width
        new_height = int(new_width * aspect_ratio)
    else:
        new_height = 1024
        aspect_ratio = original_width / original_height
        new_width = int(new_height * aspect_ratio)

    new_width = (new_width // 8) * 8
    new_height = (new_height // 8) * 8

    return new_width, new_height

@spaces.GPU
def infer(
    images,
    prompt,
    seed,
    randomize_seed,
    guidance_scale,
    steps,
    progress=gr.Progress(track_tqdm=True)
):
    gc.collect()
    torch.cuda.empty_cache()

    if not images:
        raise gr.Error("Please upload at least one image to edit.")

    pil_images =[]
    if images is not None:
        for item in images:
            try:
                if isinstance(item, tuple) or isinstance(item, list):
                    path_or_img = item[0]
                else:
                    path_or_img = item

                if isinstance(path_or_img, str):
                    pil_images.append(Image.open(path_or_img).convert("RGB"))
                elif isinstance(path_or_img, Image.Image):
                    pil_images.append(path_or_img.convert("RGB"))
                elif isinstance(path_or_img, np.ndarray):
                    pil_images.append(Image.fromarray(path_or_img).convert("RGB"))
                else:
                    pil_images.append(Image.open(path_or_img.name).convert("RGB"))
            except Exception as e:
                print(f"Skipping invalid image item: {e}")
                continue

    if not pil_images:
        raise gr.Error("Could not process uploaded images.")

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

    generator = torch.Generator(device=device).manual_seed(seed)
    negative_prompt = "worst quality, low quality, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry"

    width, height = update_dimensions_on_upload(pil_images[0])

    try:
        result_image = pipe(
            image=pil_images,
            prompt=prompt,
            negative_prompt=negative_prompt,
            height=height,
            width=width,
            num_inference_steps=steps,
            generator=generator,
            true_cfg_scale=guidance_scale,
        ).images[0]

        return result_image, seed

    except Exception as e:
        raise e
    finally:
        gc.collect()
        torch.cuda.empty_cache()

@spaces.GPU
def infer_example(images, prompt):
    if not images:
        return None, 0

    if isinstance(images, str):
        images_list = [images]
    else:
        images_list = images

    result, seed = infer(
        images=images_list,
        prompt=prompt,
        seed=0,
        randomize_seed=True,
        guidance_scale=1.0,
        steps=4
    )
    return result, seed

def use_output_as_input(latest_image):
    if latest_image is None:
        raise gr.Error("No output image available to use as input. Please generate an image first.")
    # Return the image in a list because gr.Gallery expects a list of inputs
    return [latest_image]

css = """
#col-container {
    margin: 0 auto;
    max-width: 1000px;
}
#main-title h1 {font-size: 2.4em !important;}
"""

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# **FireRed-Image-Edit-1.0-Fast**", elem_id="main-title")
        gr.Markdown("Perform image edits using [FireRed-Image-Edit-1.0](https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0) with 4-step fast inference.")

        with gr.Row(equal_height=True):
            with gr.Column():
                images = gr.Gallery(
                    label="Upload Images",
                    #sources=["upload", "clipboard"],
                    type="filepath",
                    columns=2,
                    rows=1,
                    height=300,
                    allow_preview=True
                )

                prompt = gr.Text(
                    label="Edit Prompt",
                    show_label=True,
                    max_lines=2,
                    placeholder="e.g., transform into anime, upscale, change lighting...",
                )

                run_button = gr.Button("Edit Image", variant="primary")

            with gr.Column():
                output_image = gr.Image(label="Output Image", interactive=False, format="png", height=395, type="filepath")
                
                # New Button for recursive editing
                use_as_input_button = gr.Button("🔄 Use Latest Edit as Input", variant="secondary")

                with gr.Accordion("Advanced Settings", open=True):
                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4)
                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                    randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=1.0)

        gr.Examples(
            examples=[
                [["examples/1.jpg"], "cinematic polaroid with soft grain subtle vignette gentle lighting white frame handwritten photographed 'Fire-Edit' preserving realistic texture and details."],
                [["examples/2.jpg"], "Transform the image into a dotted cartoon style."],
                [["examples/3.jpeg"], "Convert it to black and white."],
                [["examples/4.jpg", "examples/5.jpg"], "Replace her glasses with the new glasses from image 1."],
                [["examples/8.jpg", "examples/9.png"], "Replace the current clothing with the clothing from the reference image 2. Keep the person’s face, hairstyle, body pose, background, lighting, and camera angle unchanged. Ensure the new outfit fits naturally with realistic fabric texture, proper shadows, folds, and accurate proportions. Match the lighting, color tone, and overall style for a seamless and high-quality result."],
                [["examples/10.jpg", "examples/11.png"], "Replace the current clothing with the clothing from the reference image 2. Keep the person’s face, hairstyle, body pose, background, lighting, and camera angle unchanged. Ensure the new outfit fits naturally with realistic fabric texture, proper shadows, folds, and accurate proportions. Match the lighting, color tone, and overall style for a seamless and high-quality result."],
            ],
            inputs=[images, prompt],
            outputs=[output_image, seed],
            fn=infer_example,
            cache_examples=False,
            label="Examples"
        )
        
        gr.Markdown("[*](https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0)This is still an experimental Space for FireRed-Image-Edit-1.0.")

    run_button.click(
        fn=infer,
        inputs=[images, prompt, seed, randomize_seed, guidance_scale, steps],
        outputs=[output_image, seed]
    )

    # Trigger to copy output -> input
    use_as_input_button.click(
        fn=use_output_as_input,
        inputs=[output_image],
        outputs=[images]
    )

if __name__ == "__main__":
    demo.queue(max_size=30).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True)