update app

app.py

@@ -1,159 +1,395 @@
 import os
-import spaces
 import gc
-import shutil
 import gradio as gr
+import numpy as np
+import spaces
 import torch
-import safetensors.torch
-from huggingface_hub import hf_hub_download, HfApi, login
-from accelerate import init_empty_weights
+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()
 
-# --- Imports from your local modules ---
-# Ensure the folder 'qwenimage' is present in the root directory
+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
+
+pipe = QwenImageEditPlusPipeline.from_pretrained(
+    "Qwen/Qwen-Image-Edit-2511",
+    transformer=QwenImageTransformer2DModel.from_pretrained(
+        "linoyts/Qwen-Image-Edit-Rapid-AIO",
+        subfolder='transformer',
+        torch_dtype=dtype,
+        device_map='cuda'
+    ),
+    torch_dtype=dtype
+).to(device)
 
-# Configuration for the specific Qwen Transformer
-TRANSFORMER_CONFIG = {
-    "attention_head_dim": 128,
-    "axes_dims_rope": [16, 56, 56],
-    "guidance_embeds": False,
-    "in_channels": 64,
-    "joint_attention_dim": 3584,
-    "num_attention_heads": 24,
-    "num_layers": 60,
-    "out_channels": 16,
-    "patch_size": 2
+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
+
+ADAPTER_SPECS = {
+    "Multiple-Angles": {
+        "repo": "dx8152/Qwen-Edit-2509-Multiple-angles",
+        "weights": "镜头转换.safetensors",
+        "adapter_name": "multiple-angles"
+    },
+    "Photo-to-Anime": {
+        "repo": "autoweeb/Qwen-Image-Edit-2509-Photo-to-Anime",
+        "weights": "Qwen-Image-Edit-2509-Photo-to-Anime_000001000.safetensors",
+        "adapter_name": "photo-to-anime"
+    },
+    "Anime-V2": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2511-Anime",
+        "weights": "Qwen-Image-Edit-2511-Anime-2000.safetensors",
+        "adapter_name": "anime-v2"
+    },
+    "Light-Migration": {
+        "repo": "dx8152/Qwen-Edit-2509-Light-Migration",
+        "weights": "参考色调.safetensors",
+        "adapter_name": "light-migration"
+    },
+    "Upscaler": {
+        "repo": "starsfriday/Qwen-Image-Edit-2511-Upscale2K",
+        "weights": "qwen_image_edit_2511_upscale.safetensors",
+        "adapter_name": "upscale-2k"
+    },
+    "Style-Transfer": {
+        "repo": "zooeyy/Style-Transfer",
+        "weights": "Style Transfer-Alpha-V0.1.safetensors",
+        "adapter_name": "style-transfer"
+    },
+    "Manga-Tone": {
+        "repo": "nappa114514/Qwen-Image-Edit-2509-Manga-Tone",
+        "weights": "tone001.safetensors",
+        "adapter_name": "manga-tone"
+    },
+    "Anything2Real": {
+        "repo": "lrzjason/Anything2Real_2601",
+        "weights": "anything2real_2601.safetensors",
+        "adapter_name": "anything2real"
+    },
+    "Fal-Multiple-Angles": {
+        "repo": "fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA",
+        "weights": "qwen-image-edit-2511-multiple-angles-lora.safetensors",
+        "adapter_name": "fal-multiple-angles"
+    },
+    "Polaroid-Photo": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2511-Polaroid-Photo",
+        "weights": "Qwen-Image-Edit-2511-Polaroid-Photo.safetensors",
+        "adapter_name": "polaroid-photo"
+    },
+    "Unblur-Anything": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2511-Unblur-Upscale",
+        "weights": "Qwen-Image-Edit-Unblur-Upscale_15.safetensors",
+        "adapter_name": "unblur-anything"
+    },
+    "Midnight-Noir-Eyes-Spotlight": {
+        "repo": "prithivMLmods/Qwen-Image-Edit-2511-Midnight-Noir-Eyes-Spotlight",
+        "weights": "Qwen-Image-Edit-2511-Midnight-Noir-Eyes-Spotlight.safetensors",
+        "adapter_name": "midnight-noir-eyes-spotlight"
+    },
+    "Hyper-Realistic-Portrait": {
+       "repo": "prithivMLmods/Qwen-Image-Edit-2511-Hyper-Realistic-Portrait",
+       "weights": "HRP_20.safetensors",
+       "adapter_name": "hyper-realistic-portrait"
+   },     
 }
 
-@spaces.GPU(duration=300)
-def convert_and_upload(hf_token, target_repo_id, private_repo):
-    """
-    Downloads raw weights, converts keys, saves locally, and uploads to HF.
-    """
-    local_dir = "converted_qwen_transformer"
-    source_repo = "Phr00t/Qwen-Image-Edit-Rapid-AIO"
-    source_filename = "v19/Qwen-Rapid-AIO-NSFW-v19.safetensors"
+LOADED_ADAPTERS = set()
+
+def update_dimensions_on_upload(image):
+    if image is None:
+        return 1024, 1024
     
-    yield f"🚀 Starting process...\nAuthenticating with Hugging Face..."
+    original_width, original_height = image.size
     
-    if not hf_token:
-        raise gr.Error("Please provide a Write-enabled Hugging Face Token.")
+    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
     
-    try:
-        login(token=hf_token)
-        api = HfApi(token=hf_token)
-    except Exception as e:
-        raise gr.Error(f"Authentication failed: {e}")
+    return new_width, new_height
 
-    # 1. Download
-    yield f"📥 Downloading {source_filename} from {source_repo}..."
-    try:
-        checkpoint_path = hf_hub_download(repo_id=source_repo, filename=source_filename)
-    except Exception as e:
-        raise gr.Error(f"Download failed: {e}")
+@spaces.GPU
+def infer(
+    images,
+    prompt,
+    lora_adapter,
+    seed,
+    randomize_seed,
+    guidance_scale,
+    steps,
+    progress=gr.Progress(track_tqdm=True)
+):
+    gc.collect()
+    torch.cuda.empty_cache()
 
-    # 2. Initialize Empty Model
-    yield "🏗️ Initializing empty model architecture..."
-    with init_empty_weights():
-        model = QwenImageTransformer2DModel(**TRANSFORMER_CONFIG)
+    if not images:
+        raise gr.Error("Please upload at least one image to edit.")
 
-    # 3. Load and Filter Keys
-    yield "🔑 Loading state dict and filtering keys (removing 'model.diffusion_model.')..."
-    try:
-        state_dict = safetensors.torch.load_file(checkpoint_path, device="cpu")
-        
-        new_state_dict = {}
-        prefix = "model.diffusion_model."
-        ignored_keys = ["__index_timestep_zero__", "iteration", "global_step"]
+    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
 
-        for key, value in state_dict.items():
-            if key in ignored_keys:
+                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"))
+                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 key.startswith(prefix):
-                new_key = key[len(prefix):]
-                new_state_dict[new_key] = value
-        
-        del state_dict
-        gc.collect()
-    except Exception as e:
-        raise gr.Error(f"Error processing keys: {e}")
 
-    # 4. Load Weights into Model
-    yield "⚖️ Loading weights into the model object..."
-    try:
-        # assign=True is needed for accelerate's init_empty_weights
-        model.load_state_dict(new_state_dict, assign=True, strict=False)
-        del new_state_dict
-        gc.collect()
-    except Exception as e:
-        raise gr.Error(f"Error loading weights into model: {e}")
+    if not pil_images:
+        raise gr.Error("Could not process uploaded images.")
 
-    # 5. Save Locally
-    if os.path.exists(local_dir):
-        shutil.rmtree(local_dir)
-    os.makedirs(local_dir, exist_ok=True)
-    
-    yield f"💾 Saving converted model to local directory: {local_dir}..."
-    try:
-        # This saves both config.json and diffusion_pytorch_model.safetensors
-        model.save_pretrained(local_dir, safe_serialization=True)
-    except Exception as e:
-        raise gr.Error(f"Error saving local model: {e}")
+    spec = ADAPTER_SPECS.get(lora_adapter)
+    if not spec:
+        raise gr.Error(f"Configuration not found for: {lora_adapter}")
+
+    adapter_name = spec["adapter_name"]
+
+    if adapter_name not in LOADED_ADAPTERS:
+        print(f"--- Downloading and Loading Adapter: {lora_adapter} ---")
+        try:
+            pipe.load_lora_weights(
+                spec["repo"], 
+                weight_name=spec["weights"], 
+                adapter_name=adapter_name
+            )
+            LOADED_ADAPTERS.add(adapter_name)
+        except Exception as e:
+            raise gr.Error(f"Failed to load adapter {lora_adapter}: {e}")
+    else:
+        print(f"--- Adapter {lora_adapter} is already loaded. ---")
+
+    pipe.set_adapters([adapter_name], 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(pil_images[0])
 
-    # 6. Upload to Hugging Face
-    yield f"☁️ Uploading to Hugging Face Repo: {target_repo_id}..."
     try:
-        # Create repo if it doesn't exist
-        api.create_repo(repo_id=target_repo_id, private=private_repo, exist_ok=True)
+        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]
         
-        api.upload_folder(
-            folder_path=local_dir,
-            repo_id=target_repo_id,
-            commit_message="Upload converted Qwen-Image-Edit Transformer"
-        )
+        return result_image, seed
+
     except Exception as e:
-        raise gr.Error(f"Upload failed: {e}")
-        
-    # Cleanup
-    shutil.rmtree(local_dir)
-    gc.collect()
-    
-    yield f"✅ Success! Model uploaded to https://huggingface.co/{target_repo_id}"
+        raise e
+    finally:
+        gc.collect()
+        torch.cuda.empty_cache()
 
-# --- Gradio UI ---
+@spaces.GPU
+def infer_example(images, prompt, lora_adapter):
+    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,
+        lora_adapter=lora_adapter,
+        seed=0,
+        randomize_seed=True,
+        guidance_scale=1.0,
+        steps=4
+    )
+    return result, seed
 
-css = """
-#col-container { max_width: 700px; margin: 0 auto; }
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 1000px;
+}
+#main-title h1 {font-size: 2.3em !important;}
 """
 
 with gr.Blocks() as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# 🔄 Qwen Transformer Converter & Uploader")
-        gr.Markdown(
-            "This tool downloads the raw checkpoints for `Qwen-Image-Edit`, extracts the transformer, "
-            "fixes the key names, and uploads the clean `diffusers`-ready model to your Hugging Face account."
+        gr.Markdown("# **Qwen-Image-Edit-2511-LoRAs-Fast**", 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-2511) adapters. Upload one or more images.")
+
+        with gr.Row(equal_height=True):
+            with gr.Column():
+                images = gr.Gallery(
+                    label="Upload Images", 
+                    type="filepath", 
+                    columns=2, 
+                    rows=1, 
+                    height=300,
+                    allow_preview=True
+                )
+                
+                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=363)
+                
+                with gr.Row():
+                    lora_adapter = gr.Dropdown(
+                        label="Choose Editing Style",
+                        choices=list(ADAPTER_SPECS.keys()),
+                        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)
+        
+        gr.Examples(
+            examples=[
+                [["examples/B.jpg"], "Transform into anime.", "Photo-to-Anime"],
+                [["examples/A.jpeg"], "Rotate the camera 45 degrees to the right.", "Multiple-Angles"],
+                [["examples/U.jpg"], "Upscale this picture to 4K resolution.", "Upscaler"],
+                [["examples/PP1.jpg"], "cinematic polaroid with soft grain subtle vignette gentle lighting white frame handwritten photographed by hf‪‪‬ preserving realistic texture and details", "Polaroid-Photo"],
+                [["examples/Z1.jpg"], "Front-right quarter view.", "Fal-Multiple-Angles"],
+                [["examples/Z2.jpg"], "Back-left quarter view.", "Fal-Multiple-Angles"],
+                [["examples/Z3.jpg"], "Left side view, Balanced, standard.", "Fal-Multiple-Angles"],
+                [["examples/HRP.jpg"], "Transform into a hyper-realistic face portrait.", "Hyper-Realistic-Portrait"],
+                [["examples/MT.jpg"], "Paint with manga tone.", "Manga-Tone"],
+                [["examples/MN.jpg"], "Transform into Midnight Noir Eyes Spotlight.", "Midnight-Noir-Eyes-Spotlight"],
+                [["examples/ST1.jpg", "examples/ST2.jpg"], "Convert Image 1 to the style of Image 2.", "Style-Transfer"],
+                [["examples/R1.jpg"], "Change the picture to realistic photograph.", "Anything2Real"],
+                [["examples/UA.jpeg"], "Unblur and upscale.", "Unblur-Anything"],
+                [["examples/L1.jpg", "examples/L2.jpg"], "Refer to the color tone, remove the original lighting from Image 1, and relight Image 1 based on the lighting and color tone of Image 2.", "Light-Migration"],
+                [["examples/P1.jpg"], "Transform into anime (while preserving the background and remaining elements maintaining realism and original details.)", "Anime-V2"],
+            ],
+            inputs=[images, prompt, lora_adapter],
+            outputs=[output_image, seed],
+            fn=infer_example,
+            cache_examples=False,
+            label="Examples"
         )
         
-        with gr.Group():
-            hf_token = gr.Textbox(
-                label="Hugging Face Token (Write Access)", 
-                placeholder="hf_...", 
-                type="password"
-            )
-            target_repo = gr.Textbox(
-                label="Target Repository ID", 
-                placeholder="username/my-converted-qwen-transformer"
-            )
-            is_private = gr.Checkbox(label="Make Repo Private", value=True)
-            
-        convert_btn = gr.Button("Convert & Upload", variant="primary")
-        status_output = gr.Textbox(label="Status Log", interactive=False, lines=6)
-
-    convert_btn.click(
-        fn=convert_and_upload,
-        inputs=[hf_token, target_repo, is_private],
-        outputs=[status_output]
+        gr.Markdown("[*](https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast)This is still an experimental Space for Qwen-Image-Edit-2511.")
+
+    run_button.click(
+        fn=infer,
+        inputs=[images, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
+        outputs=[output_image, seed]
     )
 
 if __name__ == "__main__":
-    demo.queue().launch(theme=gr.themes.Soft(), css=css)
+    demo.queue(max_size=30).launch(css=css, theme=orange_red_theme, mcp_server=True, ssr_mode=False, show_error=True)