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 import hashlib # EKLENDI: Resim hash'ini hesaplamak için # --- AYARLAR --- INPUT_DATASET_ID = "tyndreus/image-edit-logs" OUTPUT_DATASET_ID = "tyndreus/output" # --------------- 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") 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 # --- HASH HESAPLAMA --- def get_image_hash(image): # Resmi byte'lara çevirip MD5 özetini alıyoruz img_byte_arr = image.tobytes() return hashlib.md5(img_byte_arr).hexdigest() # --- HUB'A YÜKLEME --- def upload_image_to_hub(image, dataset_id, folder_prefix="images"): try: hf_token = os.environ.get("HF_TOKEN") if not hf_token: print(f"Hata: HF_TOKEN bulunamadı.") return api = HfApi(token=hf_token) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") unique_id = str(uuid.uuid4())[:8] filename = f"{folder_prefix}_{timestamp}_{unique_id}.png" temp_path = f"/tmp/{filename}" image.save(temp_path) api.upload_file( path_or_fileobj=temp_path, path_in_repo=f"{folder_prefix}/{filename}", repo_id=dataset_id, repo_type="dataset" ) os.remove(temp_path) print(f"Yüklendi: {filename} -> {dataset_id}") except Exception as e: print(f"Yükleme hatası ({dataset_id}): {e}") @spaces.GPU(duration=30) def infer( input_image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, uploaded_history_set, # STATE BURAYA PARAMETRE OLARAK GELİYOR progress=gr.Progress(track_tqdm=True) ): if input_image is None: raise gr.Error("Please upload an image to edit.") # 1. GİRDİ RESMİNİ KONTROL ET VE GEREKİRSE KAYDET original_image = input_image.convert("RGB") # Resmin hash'ini al current_image_hash = get_image_hash(original_image) # Eğer bu hash history setinde YOKSA, yükle ve sete ekle if current_image_hash not in uploaded_history_set: print("Yeni resim algılandı, yükleniyor...") upload_image_to_hub(original_image, INPUT_DATASET_ID, folder_prefix="inputs") uploaded_history_set.add(current_image_hash) else: print("Bu resim bu oturumda daha önce yüklenmiş, atlanıyor.") # ---------------------------------------------------- if lora_adapter == "Photo-to-Anime": pipe.set_adapters(["anime"], adapter_weights=[1.0]) elif lora_adapter == "Multiple-Angles": 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" 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] # 2. ÇIKTI RESMİNİ HER ZAMAN KAYDET (OUTPUT) upload_image_to_hub(result, OUTPUT_DATASET_ID, folder_prefix="generated") # Fonksiyon artık güncellenmiş history setini de geri döndürmeli return result, seed, uploaded_history_set @spaces.GPU(duration=30) def infer_example(input_image, prompt, lora_adapter): input_pil = input_image.convert("RGB") guidance_scale = 1.0 steps = 4 # Example fonksiyonu state kullanmaz, boş set gönderiyoruz result, seed, _ = infer(input_pil, prompt, lora_adapter, 0, True, guidance_scale, steps, set()) 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: # STATE TANIMLAMASI: Kullanıcının oturumu boyunca yüklediği resimlerin hash'lerini tutar uploaded_history = gr.State(value=set()) with gr.Column(elem_id="col-container"): gr.Markdown("# **RAINBO PRO 3D IMAGE EDIT**", elem_id="main-title") gr.Markdown("Perform diverse image edits using specialized [LoRA](https://huggingface.co/models?other=base_model:adapter:Qwen/Qwen-Image-Edit-2509) adapters for the [Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) model.") 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, uploaded_history], # uploaded_history GİRDİ outputs=[output_image, seed, uploaded_history] # uploaded_history GÜNCELLENMİŞ ÇIKTI ) if __name__ == "__main__": demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)