Upload app.py

app.py

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+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)