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Photo2texture

Running on Zero

Create app.py

c4402ae verified 4 days ago

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import spaces  # MUST be line 1 for ZeroGPU

# ---------------------------------------------------------------------------
# GRADIO SCHEMA BUG PATCH (Prevents TypeError on boolean schema definitions)
# ---------------------------------------------------------------------------
import gradio_client.utils as gc_utils

_orig_get_type = gc_utils.get_type
def _safe_get_type(schema):
    if not isinstance(schema, dict):
        return "Any"
    return _orig_get_type(schema)
gc_utils.get_type = _safe_get_type

_orig_json_schema_to_python_type = gc_utils._json_schema_to_python_type
def _safe_json_schema_to_python_type(schema, defs=None):
    if not isinstance(schema, dict):
        return "Any"
    return _orig_json_schema_to_python_type(schema, defs)
gc_utils._json_schema_to_python_type = _safe_json_schema_to_python_type
# ---------------------------------------------------------------------------

import tempfile
import os
import random
import torch
import numpy as np
from PIL import Image, ImageEnhance
import gradio as gr
from diffusers import StableDiffusionInpaintPipeline

pipe_inpaint = None

def get_inpaint_pipeline():
    global pipe_inpaint
    if pipe_inpaint is None:
        pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting",
            torch_dtype=torch.float16,
            safety_checker=None
        )
        pipe_inpaint.enable_attention_slicing()
    return pipe_inpaint

def shift_periodic_canvas(pil_img, shift_x_pct, shift_y_pct):
    """
    Moves/rolls the periodic texture along X and Y axes by custom percentages.
    Allows dynamic positioning of any seam or feature across the canvas.
    """
    if pil_img is None:
        return None
    arr = np.array(pil_img.convert("RGB"))
    h, w, _ = arr.shape
    
    shift_x = int(w * (shift_x_pct / 100.0))
    shift_y = int(h * (shift_y_pct / 100.0))
    
    rolled = np.roll(np.roll(arr, shift_x, axis=1), shift_y, axis=0)
    return Image.fromarray(rolled)

def create_smooth_cosine_mask(width, height, seam_pct_x=0.25, seam_pct_y=0.35, mode="Full 2D (Both Axes)"):
    """C1-continuous smooth cosine crosshair mask."""
    x = np.linspace(-0.5, 0.5, width)
    y = np.linspace(-0.5, 0.5, height)
    xx, yy = np.meshgrid(x, y)

    rx = seam_pct_x / 2.0
    ry = seam_pct_y / 2.0

    sx = np.zeros_like(xx)
    mask_x = np.abs(xx) <= rx
    sx[mask_x] = 0.5 * (1.0 + np.cos(np.pi * np.abs(xx[mask_x]) / rx))

    if "Horizontal" in mode:
        mask_final = sx
    else:
        sy = np.zeros_like(yy)
        mask_y = np.abs(yy) <= ry
        sy[mask_y] = 0.5 * (1.0 + np.cos(np.pi * np.abs(yy[mask_y]) / ry))
        mask_final = 1.0 - (1.0 - sx) * (1.0 - sy)

    mask_uint8 = (np.clip(mask_final, 0, 1) * 255).astype(np.uint8)
    return Image.fromarray(mask_uint8, mode="L")

def extract_brush_mask(editor_data):
    """Extracts background image and painted red brush layer from Gradio ImageEditor."""
    if editor_data is None:
        return None, None

    if isinstance(editor_data, dict):
        bg = editor_data.get("background") or editor_data.get("composite")
        if bg is None:
            return None, None
        bg = bg.convert("RGB")

        layers = editor_data.get("layers", [])
        if layers and len(layers) > 0:
            combined_alpha = np.zeros((bg.height, bg.width), dtype=np.uint8)
            for layer in layers:
                if layer is not None:
                    layer_rgba = layer.convert("RGBA")
                    if layer_rgba.size != (bg.width, bg.height):
                        layer_rgba = layer_rgba.resize((bg.width, bg.height), Image.Resampling.NEAREST)
                    alpha = np.array(layer_rgba.split()[-1])
                    combined_alpha = np.maximum(combined_alpha, alpha)

            if np.max(combined_alpha) > 0:
                mask_arr = np.where(combined_alpha > 10, 255, 0).astype(np.uint8)
                return bg, Image.fromarray(mask_arr, mode="L")

    return editor_data.convert("RGB"), None

def create_tiled_preview(pil_img, mode="Horizontal Only (Landscapes/City Facades)"):
    preview_img = pil_img.copy()
    if preview_img.width > 1024:
        preview_img = preview_img.resize((1024, 1024), Image.Resampling.LANCZOS)

    w, h = preview_img.size

    if "Horizontal" in mode:
        tiled = Image.new("RGB", (w * 3, h))
        for c in range(3):
            tiled.paste(preview_img, (c * w, 0))
    else:
        tiled = Image.new("RGB", (w * 3, h * 3))
        for r in range(3):
            for c in range(3):
                tiled.paste(preview_img, (c * w, r * h))

    return tiled

def export_files(scaled_result, resolution_str, ut99_quantize):
    res_val = int(resolution_str.split("x")[0])
    temp_dir = tempfile.mkdtemp()
    pcx_path = os.path.join(temp_dir, f"ut99_texture_{res_val}.pcx")
    bmp_path = os.path.join(temp_dir, f"ut99_texture_{res_val}.bmp")

    if ut99_quantize:
        quantized = scaled_result.convert("P", palette=Image.Palette.ADAPTIVE, colors=256)
        quantized.save(pcx_path, format="PCX")
        quantized.save(bmp_path, format="BMP")
        display_img = quantized.convert("RGB")
    else:
        scaled_result.save(pcx_path, format="PCX")
        scaled_result.save(bmp_path, format="BMP")
        display_img = scaled_result

    return display_img, pcx_path, bmp_path

@spaces.GPU
def auto_generate_seamless(
    input_image, prompt, negative_prompt, tiling_mode,
    shift_x_pct, shift_y_pct, seam_width_pct, seam_height_pct, steps, guidance_scale, seed_val,
    sharpness_boost, resolution_str, ut99_quantize
):
    if input_image is None:
        return None, None, None, None, None, "⚠️ Please upload a source photo first."

    actual_seed = random.randint(0, 2147483647) if (seed_val == -1 or seed_val is None) else int(seed_val)
    res_val = int(resolution_str.split("x")[0])

    pipeline = get_inpaint_pipeline()
    pipeline.to("cuda")
    generator = torch.Generator("cuda").manual_seed(actual_seed)

    original_photo = input_image.convert("RGB")
    proc_photo = original_photo.resize((512, 512), Image.Resampling.LANCZOS)
    
    # Apply user-selected X and Y periodic movement/roll
    shifted_photo = shift_periodic_canvas(proc_photo, shift_x_pct, shift_y_pct)

    user_steps = max(10, int(steps))
    user_guidance = float(guidance_scale)

    photo_prompt = prompt + ", raw photo, ultra realistic, sharp photographic detail, DSLR 8k, crisp texture, natural lighting"
    strict_negative = negative_prompt + ", oil painting, painting, illustration, drawing, 3d render, anime, smooth, blurry, artistic, cartoon, canvas texture, digital art, vector"

    seam_mask_cosine = create_smooth_cosine_mask(
        512, 512,
        seam_pct_x=seam_width_pct / 100.0,
        seam_pct_y=seam_height_pct / 100.0,
        mode=tiling_mode
    )

    inpainted_zone = pipeline(
        prompt=photo_prompt,
        negative_prompt=strict_negative,
        image=shifted_photo,
        mask_image=seam_mask_cosine,
        num_inference_steps=user_steps,
        guidance_scale=user_guidance,
        generator=generator
    ).images[0]

    seamless_result = Image.composite(inpainted_zone, shifted_photo, seam_mask_cosine)
    scaled_result = seamless_result.resize((res_val, res_val), Image.Resampling.LANCZOS)

    if sharpness_boost > 1.0:
        scaled_result = ImageEnhance.Sharpness(scaled_result).enhance(sharpness_boost)

    tiled_preview = create_tiled_preview(scaled_result, mode=tiling_mode)
    display_img, pcx_path, bmp_path = export_files(scaled_result, resolution_str, ut99_quantize)

    editor_update = {"background": scaled_result, "layers": [], "composite": scaled_result}
    status_msg = f"✅ Seamless Complete | Shift X: {shift_x_pct}%, Shift Y: {shift_y_pct}% | Seed: {actual_seed}"

    return editor_update, display_img, tiled_preview, pcx_path, bmp_path, status_msg

@spaces.GPU
def manual_edge_repair(
    editor_data, prompt, negative_prompt, tiling_mode,
    steps, guidance_scale, seed_val, sharpness_boost, resolution_str, ut99_quantize
):
    bg_img, mask_img = extract_brush_mask(editor_data)

    if bg_img is None or mask_img is None:
        return None, None, None, None, None, "⚠️ Paint red brush strokes over problem areas first."

    actual_seed = random.randint(0, 2147483647) if (seed_val == -1 or seed_val is None) else int(seed_val)
    res_val = int(resolution_str.split("x")[0])

    pipeline = get_inpaint_pipeline()
    pipeline.to("cuda")
    generator = torch.Generator("cuda").manual_seed(actual_seed)

    init_512 = bg_img.resize((512, 512), Image.Resampling.LANCZOS)
    mask_512 = mask_img.resize((512, 512), Image.Resampling.NEAREST)

    user_steps = max(10, int(steps))
    user_guidance = float(guidance_scale)

    photo_prompt = prompt + ", raw photo, ultra realistic, sharp photographic detail, DSLR 8k, seamless texture blending"
    strict_negative = negative_prompt + ", oil painting, painting, illustration, drawing, 3d render, anime, smooth, blurry, artistic, cartoon"

    repaired_512 = pipeline(
        prompt=photo_prompt,
        negative_prompt=strict_negative,
        image=init_512,
        mask_image=mask_512,
        num_inference_steps=user_steps,
        guidance_scale=user_guidance,
        generator=generator
    ).images[0]

    scaled_result = repaired_512.resize((res_val, res_val), Image.Resampling.LANCZOS)

    if sharpness_boost > 1.0:
        scaled_result = ImageEnhance.Sharpness(scaled_result).enhance(sharpness_boost)

    tiled_preview = create_tiled_preview(scaled_result, mode=tiling_mode)
    display_img, pcx_path, bmp_path = export_files(scaled_result, resolution_str, ut99_quantize)

    editor_update = {"background": scaled_result, "layers": [], "composite": scaled_result}
    status_msg = f"🩹 Edge Repair Complete | Seed: {actual_seed}"

    return editor_update, display_img, tiled_preview, pcx_path, bmp_path, status_msg

def update_canvas_roll(editor_data, shift_x_pct, shift_y_pct):
    """Interactive button action to roll/move periodic texture tiles live on the editor canvas."""
    if editor_data is None:
        return None
    if isinstance(editor_data, dict):
        bg = editor_data.get("background") or editor_data.get("composite")
        if bg is None:
            return None
        pil_img = bg.convert("RGB")
    else:
        pil_img = editor_data.convert("RGB")

    rolled = shift_periodic_canvas(pil_img, shift_x_pct, shift_y_pct)
    return {"background": rolled, "layers": [], "composite": rolled}

# Mobile Clean Layout
mobile_css = """
.gradio-container { padding: 6px !important; max-width: 100% !important; }
.mobile-btn { width: 100% !important; font-size: 16px !important; padding: 12px !important; }
"""

with gr.Blocks(title="UT99 Mobile Texture Forge", css=mobile_css) as demo:
    gr.Markdown("### 📱 UT99 ZeroGPU Mobile Texture Forge")

    output_status = gr.Textbox(label="Status Log", value="Ready", interactive=False)

    with gr.Tabs():
        # TAB 1: GENERATE & PERIODIC MOVEMENT
        with gr.Tab("1. Generate & Move Tiles"):
            input_img = gr.Image(type="pil", label="Upload Photo")

            tiling_mode = gr.Radio(
                choices=["Horizontal Only (Landscapes/City Facades)", "Full 2D (Both Axes)"],
                value="Full 2D (Both Axes)",
                label="Seamless Mode"
            )

            gr.Markdown("#### ↔️ Periodic Tile Movement (Move seams/tiles on canvas)")
            with gr.Row():
                shift_x_pct = gr.Slider(minimum=0, maximum=100, value=50, step=5, label="Move X-Axis (%)")
                shift_y_pct = gr.Slider(minimum=0, maximum=100, value=50, step=5, label="Move Y-Axis (%)")

            prompt = gr.Textbox(
                value="Detailed fabric curtain texture, sharp photographic pattern",
                label="AI Prompt"
            )

            negative_prompt = gr.Textbox(
                value="seam line, border, frame, edge line, blur, watermark, repeating grid",
                label="Negative Prompt"
            )

            with gr.Accordion("Advanced Options (Seam Mask Widths, CFG, Seed)", open=False):
                with gr.Row():
                    seam_width_pct = gr.Slider(minimum=10, maximum=50, value=30, step=5, label="Horiz Seam (%)")
                    seam_height_pct = gr.Slider(minimum=10, maximum=60, value=35, step=5, label="Vert Seam (%)")

                with gr.Row():
                    steps = gr.Slider(minimum=10, maximum=50, value=25, step=1, label="Steps")
                    guidance_scale = gr.Slider(minimum=1.0, maximum=15.0, value=7.5, step=0.5, label="CFG")

                with gr.Row():
                    sharpness_boost = gr.Slider(minimum=1.0, maximum=2.5, value=1.3, step=0.1, label="Sharpness")
                    seed_val = gr.Number(value=-1, precision=0, label="Seed (-1 = Random)")

                resolution = gr.Dropdown(
                    choices=["128x128", "256x256 (Default UT99)", "512x512 (HD UT99)", "1024x1024", "2048x2048 (2K)", "4096x4096 (4K)", "8192x8192 (8K)"],
                    value="1024x1024",
                    label="Resolution"
                )

                ut99_quantize = gr.Checkbox(value=False, label="Convert to 8-Bit / 256 Colors (.PCX / .BMP)")

            btn_auto = gr.Button("🚀 Generate Seamless Texture", variant="primary", elem_classes=["mobile-btn"])

        # TAB 2: MANUAL CANVAS TOUCH-UP
        with gr.Tab("2. Touch-Up & Move Canvas"):
            gr.Markdown("Draw red brush strokes over problem areas, or move the canvas tiles:")
            
            with gr.Row():
                btn_roll_live = gr.Button("↔️ Roll / Move Canvas Tiles", variant="secondary")

            editor = gr.ImageEditor(
                type="pil",
                label="Touch-Up Canvas",
                brush=gr.Brush(colors=["#FF0000"], default_size=15),
                interactive=True
            )
            btn_repair = gr.Button("🩹 Repair Red Painted Lines", variant="primary", elem_classes=["mobile-btn"])

        # TAB 3: PREVIEW & DOWNLOAD
        with gr.Tab("3. Preview & Export"):
            output_texture = gr.Image(type="pil", label="Seamless Texture Output")
            output_preview = gr.Image(type="pil", label="Tiling Preview Test")

            with gr.Row():
                pcx_file = gr.File(label="Download .PCX")
                bmp_file = gr.File(label="Download .BMP")

    # Action Connections
    btn_auto.click(
        fn=auto_generate_seamless,
        inputs=[
            input_img, prompt, negative_prompt, tiling_mode,
            shift_x_pct, shift_y_pct, seam_width_pct, seam_height_pct,
            steps, guidance_scale, seed_val, sharpness_boost, resolution, ut99_quantize
        ],
        outputs=[editor, output_texture, output_preview, pcx_file, bmp_file, output_status]
    )

    btn_roll_live.click(
        fn=update_canvas_roll,
        inputs=[editor, shift_x_pct, shift_y_pct],
        outputs=[editor]
    )

    btn_repair.click(
        fn=manual_edge_repair,
        inputs=[
            editor, prompt, negative_prompt, tiling_mode,
            steps, guidance_scale, seed_val, sharpness_boost, resolution, ut99_quantize
        ],
        outputs=[editor, output_texture, output_preview, pcx_file, bmp_file, output_status]
    )

if __name__ == "__main__":
    demo.launch(show_api=False)
    

	import spaces # MUST be line 1 for ZeroGPU

	# ---------------------------------------------------------------------------
	# GRADIO SCHEMA BUG PATCH (Prevents TypeError on boolean schema definitions)
	# ---------------------------------------------------------------------------
	import gradio_client.utils as gc_utils

	_orig_get_type = gc_utils.get_type
	def _safe_get_type(schema):
	if not isinstance(schema, dict):
	return "Any"
	return _orig_get_type(schema)
	gc_utils.get_type = _safe_get_type

	_orig_json_schema_to_python_type = gc_utils._json_schema_to_python_type
	def _safe_json_schema_to_python_type(schema, defs=None):
	if not isinstance(schema, dict):
	return "Any"
	return _orig_json_schema_to_python_type(schema, defs)
	gc_utils._json_schema_to_python_type = _safe_json_schema_to_python_type
	# ---------------------------------------------------------------------------

	import tempfile
	import os
	import random
	import torch
	import numpy as np
	from PIL import Image, ImageEnhance
	import gradio as gr
	from diffusers import StableDiffusionInpaintPipeline

	pipe_inpaint = None

	def get_inpaint_pipeline():
	global pipe_inpaint
	if pipe_inpaint is None:
	pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(
	"runwayml/stable-diffusion-inpainting",
	torch_dtype=torch.float16,
	safety_checker=None
	)
	pipe_inpaint.enable_attention_slicing()
	return pipe_inpaint

	def shift_periodic_canvas(pil_img, shift_x_pct, shift_y_pct):
	"""
	Moves/rolls the periodic texture along X and Y axes by custom percentages.
	Allows dynamic positioning of any seam or feature across the canvas.
	"""
	if pil_img is None:
	return None
	arr = np.array(pil_img.convert("RGB"))
	h, w, _ = arr.shape

	shift_x = int(w * (shift_x_pct / 100.0))
	shift_y = int(h * (shift_y_pct / 100.0))

	rolled = np.roll(np.roll(arr, shift_x, axis=1), shift_y, axis=0)
	return Image.fromarray(rolled)

	def create_smooth_cosine_mask(width, height, seam_pct_x=0.25, seam_pct_y=0.35, mode="Full 2D (Both Axes)"):
	"""C1-continuous smooth cosine crosshair mask."""
	x = np.linspace(-0.5, 0.5, width)
	y = np.linspace(-0.5, 0.5, height)
	xx, yy = np.meshgrid(x, y)

	rx = seam_pct_x / 2.0
	ry = seam_pct_y / 2.0

	sx = np.zeros_like(xx)
	mask_x = np.abs(xx) <= rx
	sx[mask_x] = 0.5 * (1.0 + np.cos(np.pi * np.abs(xx[mask_x]) / rx))

	if "Horizontal" in mode:
	mask_final = sx
	else:
	sy = np.zeros_like(yy)
	mask_y = np.abs(yy) <= ry
	sy[mask_y] = 0.5 * (1.0 + np.cos(np.pi * np.abs(yy[mask_y]) / ry))
	mask_final = 1.0 - (1.0 - sx) * (1.0 - sy)

	mask_uint8 = (np.clip(mask_final, 0, 1) * 255).astype(np.uint8)
	return Image.fromarray(mask_uint8, mode="L")

	def extract_brush_mask(editor_data):
	"""Extracts background image and painted red brush layer from Gradio ImageEditor."""
	if editor_data is None:
	return None, None

	if isinstance(editor_data, dict):
	bg = editor_data.get("background") or editor_data.get("composite")
	if bg is None:
	return None, None
	bg = bg.convert("RGB")

	layers = editor_data.get("layers", [])
	if layers and len(layers) > 0:
	combined_alpha = np.zeros((bg.height, bg.width), dtype=np.uint8)
	for layer in layers:
	if layer is not None:
	layer_rgba = layer.convert("RGBA")
	if layer_rgba.size != (bg.width, bg.height):
	layer_rgba = layer_rgba.resize((bg.width, bg.height), Image.Resampling.NEAREST)
	alpha = np.array(layer_rgba.split()[-1])
	combined_alpha = np.maximum(combined_alpha, alpha)

	if np.max(combined_alpha) > 0:
	mask_arr = np.where(combined_alpha > 10, 255, 0).astype(np.uint8)
	return bg, Image.fromarray(mask_arr, mode="L")

	return editor_data.convert("RGB"), None

	def create_tiled_preview(pil_img, mode="Horizontal Only (Landscapes/City Facades)"):
	preview_img = pil_img.copy()
	if preview_img.width > 1024:
	preview_img = preview_img.resize((1024, 1024), Image.Resampling.LANCZOS)

	w, h = preview_img.size

	if "Horizontal" in mode:
	tiled = Image.new("RGB", (w * 3, h))
	for c in range(3):
	tiled.paste(preview_img, (c * w, 0))
	else:
	tiled = Image.new("RGB", (w * 3, h * 3))
	for r in range(3):
	for c in range(3):
	tiled.paste(preview_img, (c * w, r * h))

	return tiled

	def export_files(scaled_result, resolution_str, ut99_quantize):
	res_val = int(resolution_str.split("x")[0])
	temp_dir = tempfile.mkdtemp()
	pcx_path = os.path.join(temp_dir, f"ut99_texture_{res_val}.pcx")
	bmp_path = os.path.join(temp_dir, f"ut99_texture_{res_val}.bmp")

	if ut99_quantize:
	quantized = scaled_result.convert("P", palette=Image.Palette.ADAPTIVE, colors=256)
	quantized.save(pcx_path, format="PCX")
	quantized.save(bmp_path, format="BMP")
	display_img = quantized.convert("RGB")
	else:
	scaled_result.save(pcx_path, format="PCX")
	scaled_result.save(bmp_path, format="BMP")
	display_img = scaled_result

	return display_img, pcx_path, bmp_path

	@spaces.GPU
	def auto_generate_seamless(
	input_image, prompt, negative_prompt, tiling_mode,
	shift_x_pct, shift_y_pct, seam_width_pct, seam_height_pct, steps, guidance_scale, seed_val,
	sharpness_boost, resolution_str, ut99_quantize
	):
	if input_image is None:
	return None, None, None, None, None, "⚠️ Please upload a source photo first."

	actual_seed = random.randint(0, 2147483647) if (seed_val == -1 or seed_val is None) else int(seed_val)
	res_val = int(resolution_str.split("x")[0])

	pipeline = get_inpaint_pipeline()
	pipeline.to("cuda")
	generator = torch.Generator("cuda").manual_seed(actual_seed)

	original_photo = input_image.convert("RGB")
	proc_photo = original_photo.resize((512, 512), Image.Resampling.LANCZOS)

	# Apply user-selected X and Y periodic movement/roll
	shifted_photo = shift_periodic_canvas(proc_photo, shift_x_pct, shift_y_pct)

	user_steps = max(10, int(steps))
	user_guidance = float(guidance_scale)

	photo_prompt = prompt + ", raw photo, ultra realistic, sharp photographic detail, DSLR 8k, crisp texture, natural lighting"
	strict_negative = negative_prompt + ", oil painting, painting, illustration, drawing, 3d render, anime, smooth, blurry, artistic, cartoon, canvas texture, digital art, vector"

	seam_mask_cosine = create_smooth_cosine_mask(
	512, 512,
	seam_pct_x=seam_width_pct / 100.0,
	seam_pct_y=seam_height_pct / 100.0,
	mode=tiling_mode
	)

	inpainted_zone = pipeline(
	prompt=photo_prompt,
	negative_prompt=strict_negative,
	image=shifted_photo,
	mask_image=seam_mask_cosine,
	num_inference_steps=user_steps,
	guidance_scale=user_guidance,
	generator=generator
	).images[0]

	seamless_result = Image.composite(inpainted_zone, shifted_photo, seam_mask_cosine)
	scaled_result = seamless_result.resize((res_val, res_val), Image.Resampling.LANCZOS)

	if sharpness_boost > 1.0:
	scaled_result = ImageEnhance.Sharpness(scaled_result).enhance(sharpness_boost)

	tiled_preview = create_tiled_preview(scaled_result, mode=tiling_mode)
	display_img, pcx_path, bmp_path = export_files(scaled_result, resolution_str, ut99_quantize)

	editor_update = {"background": scaled_result, "layers": [], "composite": scaled_result}
	status_msg = f"✅ Seamless Complete \| Shift X: {shift_x_pct}%, Shift Y: {shift_y_pct}% \| Seed: {actual_seed}"

	return editor_update, display_img, tiled_preview, pcx_path, bmp_path, status_msg

	@spaces.GPU
	def manual_edge_repair(
	editor_data, prompt, negative_prompt, tiling_mode,
	steps, guidance_scale, seed_val, sharpness_boost, resolution_str, ut99_quantize
	):
	bg_img, mask_img = extract_brush_mask(editor_data)

	if bg_img is None or mask_img is None:
	return None, None, None, None, None, "⚠️ Paint red brush strokes over problem areas first."

	actual_seed = random.randint(0, 2147483647) if (seed_val == -1 or seed_val is None) else int(seed_val)
	res_val = int(resolution_str.split("x")[0])

	pipeline = get_inpaint_pipeline()
	pipeline.to("cuda")
	generator = torch.Generator("cuda").manual_seed(actual_seed)

	init_512 = bg_img.resize((512, 512), Image.Resampling.LANCZOS)
	mask_512 = mask_img.resize((512, 512), Image.Resampling.NEAREST)

	user_steps = max(10, int(steps))
	user_guidance = float(guidance_scale)

	photo_prompt = prompt + ", raw photo, ultra realistic, sharp photographic detail, DSLR 8k, seamless texture blending"
	strict_negative = negative_prompt + ", oil painting, painting, illustration, drawing, 3d render, anime, smooth, blurry, artistic, cartoon"

	repaired_512 = pipeline(
	prompt=photo_prompt,
	negative_prompt=strict_negative,
	image=init_512,
	mask_image=mask_512,
	num_inference_steps=user_steps,
	guidance_scale=user_guidance,
	generator=generator
	).images[0]

	scaled_result = repaired_512.resize((res_val, res_val), Image.Resampling.LANCZOS)

	if sharpness_boost > 1.0:
	scaled_result = ImageEnhance.Sharpness(scaled_result).enhance(sharpness_boost)

	tiled_preview = create_tiled_preview(scaled_result, mode=tiling_mode)
	display_img, pcx_path, bmp_path = export_files(scaled_result, resolution_str, ut99_quantize)

	editor_update = {"background": scaled_result, "layers": [], "composite": scaled_result}
	status_msg = f"🩹 Edge Repair Complete \| Seed: {actual_seed}"

	return editor_update, display_img, tiled_preview, pcx_path, bmp_path, status_msg

	def update_canvas_roll(editor_data, shift_x_pct, shift_y_pct):
	"""Interactive button action to roll/move periodic texture tiles live on the editor canvas."""
	if editor_data is None:
	return None
	if isinstance(editor_data, dict):
	bg = editor_data.get("background") or editor_data.get("composite")
	if bg is None:
	return None
	pil_img = bg.convert("RGB")
	else:
	pil_img = editor_data.convert("RGB")

	rolled = shift_periodic_canvas(pil_img, shift_x_pct, shift_y_pct)
	return {"background": rolled, "layers": [], "composite": rolled}

	# Mobile Clean Layout
	mobile_css = """
	.gradio-container { padding: 6px !important; max-width: 100% !important; }
	.mobile-btn { width: 100% !important; font-size: 16px !important; padding: 12px !important; }
	"""

	with gr.Blocks(title="UT99 Mobile Texture Forge", css=mobile_css) as demo:
	gr.Markdown("### 📱 UT99 ZeroGPU Mobile Texture Forge")

	output_status = gr.Textbox(label="Status Log", value="Ready", interactive=False)

	with gr.Tabs():
	# TAB 1: GENERATE & PERIODIC MOVEMENT
	with gr.Tab("1. Generate & Move Tiles"):
	input_img = gr.Image(type="pil", label="Upload Photo")

	tiling_mode = gr.Radio(
	choices=["Horizontal Only (Landscapes/City Facades)", "Full 2D (Both Axes)"],
	value="Full 2D (Both Axes)",
	label="Seamless Mode"
	)

	gr.Markdown("#### ↔️ Periodic Tile Movement (Move seams/tiles on canvas)")
	with gr.Row():
	shift_x_pct = gr.Slider(minimum=0, maximum=100, value=50, step=5, label="Move X-Axis (%)")
	shift_y_pct = gr.Slider(minimum=0, maximum=100, value=50, step=5, label="Move Y-Axis (%)")

	prompt = gr.Textbox(
	value="Detailed fabric curtain texture, sharp photographic pattern",
	label="AI Prompt"
	)

	negative_prompt = gr.Textbox(
	value="seam line, border, frame, edge line, blur, watermark, repeating grid",
	label="Negative Prompt"
	)

	with gr.Accordion("Advanced Options (Seam Mask Widths, CFG, Seed)", open=False):
	with gr.Row():
	seam_width_pct = gr.Slider(minimum=10, maximum=50, value=30, step=5, label="Horiz Seam (%)")
	seam_height_pct = gr.Slider(minimum=10, maximum=60, value=35, step=5, label="Vert Seam (%)")

	with gr.Row():
	steps = gr.Slider(minimum=10, maximum=50, value=25, step=1, label="Steps")
	guidance_scale = gr.Slider(minimum=1.0, maximum=15.0, value=7.5, step=0.5, label="CFG")

	with gr.Row():
	sharpness_boost = gr.Slider(minimum=1.0, maximum=2.5, value=1.3, step=0.1, label="Sharpness")
	seed_val = gr.Number(value=-1, precision=0, label="Seed (-1 = Random)")

	resolution = gr.Dropdown(
	choices=["128x128", "256x256 (Default UT99)", "512x512 (HD UT99)", "1024x1024", "2048x2048 (2K)", "4096x4096 (4K)", "8192x8192 (8K)"],
	value="1024x1024",
	label="Resolution"
	)

	ut99_quantize = gr.Checkbox(value=False, label="Convert to 8-Bit / 256 Colors (.PCX / .BMP)")

	btn_auto = gr.Button("🚀 Generate Seamless Texture", variant="primary", elem_classes=["mobile-btn"])

	# TAB 2: MANUAL CANVAS TOUCH-UP
	with gr.Tab("2. Touch-Up & Move Canvas"):
	gr.Markdown("Draw red brush strokes over problem areas, or move the canvas tiles:")

	with gr.Row():
	btn_roll_live = gr.Button("↔️ Roll / Move Canvas Tiles", variant="secondary")

	editor = gr.ImageEditor(
	type="pil",
	label="Touch-Up Canvas",
	brush=gr.Brush(colors=["#FF0000"], default_size=15),
	interactive=True
	)
	btn_repair = gr.Button("🩹 Repair Red Painted Lines", variant="primary", elem_classes=["mobile-btn"])

	# TAB 3: PREVIEW & DOWNLOAD
	with gr.Tab("3. Preview & Export"):
	output_texture = gr.Image(type="pil", label="Seamless Texture Output")
	output_preview = gr.Image(type="pil", label="Tiling Preview Test")

	with gr.Row():
	pcx_file = gr.File(label="Download .PCX")
	bmp_file = gr.File(label="Download .BMP")

	# Action Connections
	btn_auto.click(
	fn=auto_generate_seamless,
	inputs=[
	input_img, prompt, negative_prompt, tiling_mode,
	shift_x_pct, shift_y_pct, seam_width_pct, seam_height_pct,
	steps, guidance_scale, seed_val, sharpness_boost, resolution, ut99_quantize
	],
	outputs=[editor, output_texture, output_preview, pcx_file, bmp_file, output_status]
	)

	btn_roll_live.click(
	fn=update_canvas_roll,
	inputs=[editor, shift_x_pct, shift_y_pct],
	outputs=[editor]
	)

	btn_repair.click(
	fn=manual_edge_repair,
	inputs=[
	editor, prompt, negative_prompt, tiling_mode,
	steps, guidance_scale, seed_val, sharpness_boost, resolution, ut99_quantize
	],
	outputs=[editor, output_texture, output_preview, pcx_file, bmp_file, output_status]
	)

	if __name__ == "__main__":
	demo.launch(show_api=False)