Update app.py

app.py

@@ -28,354 +28,6 @@ from PIL import Image
 import os
 import gradio as gr
 
-def turn_into_video(input_images, output_images, prompt, progress=gr.Progress(track_tqdm=True)):
-    if not input_images or not output_images:
-        raise gr.Error("Please generate an output image first.")
-
-    progress(0.02, desc="Preparing images...")
-
-    def extract_pil(img_entry):
-        if isinstance(img_entry, tuple) and isinstance(img_entry[0], Image.Image):
-            return img_entry[0]
-        elif isinstance(img_entry, Image.Image):
-            return img_entry
-        elif isinstance(img_entry, str):
-            return Image.open(img_entry)
-        else:
-            raise gr.Error(f"Unsupported image format: {type(img_entry)}")
-
-    start_img = extract_pil(input_images[0])
-    end_img   = extract_pil(output_images[0])
-
-    progress(0.10, desc="Saving temp files...")
-
-    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_start, \
-         tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_end:
-        start_img.save(tmp_start.name)
-        end_img.save(tmp_end.name)
-
-    progress(0.20, desc="Connecting to Wan space...")
-
-    client = Client("multimodalart/wan-2-2-first-last-frame")  
-
-    progress(0.35, desc="Generating video...")
-
-    video_path, seed = client.predict(
-        start_image_pil=handle_file(tmp_start.name),
-        end_image_pil=handle_file(tmp_end.name),
-        prompt=prompt or "smooth cinematic transition",
-        api_name="/generate_video"
-    )
-
-    progress(0.95, desc="Finalizing...")
-    print(video_path)
-    return video_path['video']
-
-
-
-
-SYSTEM_PROMPT = '''
-# Edit Instruction Rewriter
-You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable professional-level edit instruction based on the user-provided instruction and the image to be edited.  
-Please strictly follow the rewriting rules below:
-## 1. General Principles
-- Keep the rewritten prompt **concise and comprehensive**. Avoid overly long sentences and unnecessary descriptive language.  
-- If the instruction is contradictory, vague, or unachievable, prioritize reasonable inference and correction, and supplement details when necessary.  
-- Keep the main part of the original instruction unchanged, only enhancing its clarity, rationality, and visual feasibility.  
-- All added objects or modifications must align with the logic and style of the scene in the input images.  
-- If multiple sub-images are to be generated, describe the content of each sub-image individually.  
-## 2. Task-Type Handling Rules
-### 1. Add, Delete, Replace Tasks
-- If the instruction is clear (already includes task type, target entity, position, quantity, attributes), preserve the original intent and only refine the grammar.  
-- If the description is vague, supplement with minimal but sufficient details (category, color, size, orientation, position, etc.). For example:  
-    > Original: "Add an animal"  
-    > Rewritten: "Add a light-gray cat in the bottom-right corner, sitting and facing the camera"  
-- Remove meaningless instructions: e.g., "Add 0 objects" should be ignored or flagged as invalid.  
-- For replacement tasks, specify "Replace Y with X" and briefly describe the key visual features of X.  
-### 2. Text Editing Tasks
-- All text content must be enclosed in English double quotes `" "`. Keep the original language of the text, and keep the capitalization.  
-- Both adding new text and replacing existing text are text replacement tasks, For example:  
-    - Replace "xx" to "yy"  
-    - Replace the mask / bounding box to "yy"  
-    - Replace the visual object to "yy"  
-- Specify text position, color, and layout only if user has required.  
-- If font is specified, keep the original language of the font.  
-### 3. Human Editing Tasks
-- Make the smallest changes to the given user's prompt.  
-- If changes to background, action, expression, camera shot, or ambient lighting are required, please list each modification individually.
-- **Edits to makeup or facial features / expression must be subtle, not exaggerated, and must preserve the subject's identity consistency.**
-    > Original: "Add eyebrows to the face"  
-    > Rewritten: "Slightly thicken the person's eyebrows with little change, look natural."
-### 4. Style Conversion or Enhancement Tasks
-- If a style is specified, describe it concisely using key visual features. For example:  
-    > Original: "Disco style"  
-    > Rewritten: "1970s disco style: flashing lights, disco ball, mirrored walls, vibrant colors"  
-- For style reference, analyze the original image and extract key characteristics (color, composition, texture, lighting, artistic style, etc.), integrating them into the instruction.  
-- **Colorization tasks (including old photo restoration) must use the fixed template:**  
-  "Restore and colorize the old photo."  
-- Clearly specify the object to be modified. For example:  
-    > Original: Modify the subject in Picture 1 to match the style of Picture 2.  
-    > Rewritten: Change the girl in Picture 1 to the ink-wash style of Picture 2 — rendered in black-and-white watercolor with soft color transitions.
-### 5. Material Replacement
-- Clearly specify the object and the material. For example: "Change the material of the apple to papercut style."
-- For text material replacement, use the fixed template:
-    "Change the material of text "xxxx" to laser style"
-### 6. Logo/Pattern Editing
-- Material replacement should preserve the original shape and structure as much as possible. For example:
-   > Original: "Convert to sapphire material"  
-   > Rewritten: "Convert the main subject in the image to sapphire material, preserving similar shape and structure"
-- When migrating logos/patterns to new scenes, ensure shape and structure consistency. For example:
-   > Original: "Migrate the logo in the image to a new scene"  
-   > Rewritten: "Migrate the logo in the image to a new scene, preserving similar shape and structure"
-### 7. Multi-Image Tasks
-- Rewritten prompts must clearly point out which image's element is being modified. For example:  
-    > Original: "Replace the subject of picture 1 with the subject of picture 2"  
-    > Rewritten: "Replace the girl of picture 1 with the boy of picture 2, keeping picture 2's background unchanged"  
-- For stylization tasks, describe the reference image's style in the rewritten prompt, while preserving the visual content of the source image.  
-## 3. Rationale and Logic Check
-- Resolve contradictory instructions: e.g., "Remove all trees but keep all trees" requires logical correction.
-- Supplement missing critical information: e.g., if position is unspecified, choose a reasonable area based on composition (near subject, blank space, center/edge, etc.).
-# Output Format Example
-```json
-{
-   "Rewritten": "..."
-}
-'''
-
-
-NEXT_SCENE_SYSTEM_PROMPT = '''
-# Next Scene Prompt Generator
-You are a cinematic AI director assistant. Your task is to analyze the provided image and generate a compelling "Next Scene" prompt that describes the natural cinematic progression from the current frame.
-## Core Principles:
-- Think like a film director: Consider camera dynamics, visual composition, and narrative continuity
-- Create prompts that flow seamlessly from the current frame
-- Focus on **visual progression** rather than static modifications
-- Maintain compositional coherence while introducing organic transitions
-## Prompt Structure:
-Always begin with "Next Scene: " followed by your cinematic description.
-## Key Elements to Include:
-1. **Camera Movement**: Specify one of these or combinations:
-   - Dolly shots (camera moves toward/away from subject)
-   - Push-ins or pull-backs
-   - Tracking moves (camera follows subject)
-   - Pan left/right
-   - Tilt up/down
-   - Zoom in/out
-2. **Framing Evolution**: Describe how the shot composition changes:
-   - Wide to close-up transitions
-   - Angle shifts (high angle to eye level, etc.)
-   - Reframing of subjects
-   - Revealing new elements in frame
-3. **Environmental Reveals** (if applicable):
-   - New characters entering frame
-   - Expanded scenery
-   - Spatial progression
-   - Background elements becoming visible
-4. **Atmospheric Shifts** (if enhancing the scene):
-   - Lighting changes (golden hour, shadows, lens flare)
-   - Weather evolution
-   - Time-of-day transitions
-   - Depth and mood indicators
-## Guidelines:
-- Keep descriptions concise but vivid (2-3 sentences max)
-- Always specify the camera action first
-- Focus on what changes between this frame and the next
-- Maintain the scene's existing style and mood unless intentionally transitioning
-- Prefer natural, organic progressions over abrupt changes
-## Example Outputs:
-- "Next Scene: The camera pulls back from a tight close-up on the airship to a sweeping aerial view, revealing an entire fleet of vessels soaring through a fantasy landscape."
-- "Next Scene: The camera tracks forward and tilts down, bringing the sun and helicopters closer into frame as a strong lens flare intensifies."
-- "Next Scene: The camera pans right, removing the dragon and rider from view while revealing more of the floating mountain range in the distance."
-- "Next Scene: The camera moves slightly forward as sunlight breaks through the clouds, casting a soft glow around the character's silhouette in the mist. Realistic cinematic style, atmospheric depth."
-## Output Format:
-Return ONLY the next scene prompt as plain text, starting with "Next Scene: "
-Do NOT include JSON formatting or additional explanations.
-'''
-
-# --- Prompt Enhancement using Hugging Face InferenceClient ---
-def polish_prompt_hf(original_prompt, img_list):
-    """
-    Rewrites the prompt using a Hugging Face InferenceClient.
-    """
-    # Ensure HF_TOKEN is set
-    api_key = os.environ.get("HF_TOKEN")
-    if not api_key:
-        print("Warning: HF_TOKEN not set. Falling back to original prompt.")
-        return original_prompt
-
-    try:
-        # Initialize the client
-        prompt = f"{SYSTEM_PROMPT}\n\nUser Input: {original_prompt}\n\nRewritten Prompt:"
-        client = InferenceClient(
-            provider="nebius",
-            api_key=api_key,
-        )
-
-        # Format the messages for the chat completions API
-        sys_promot = "you are a helpful assistant, you should provide useful answers to users."
-        messages = [
-            {"role": "system", "content": sys_promot},
-            {"role": "user", "content": []}]
-        for img in img_list:
-            messages[1]["content"].append(
-                {"image": f"data:image/png;base64,{encode_image(img)}"})
-        messages[1]["content"].append({"text": f"{prompt}"})
-
-        # Call the API
-        completion = client.chat.completions.create(
-            model="Qwen/Qwen2.5-VL-72B-Instruct",
-            messages=messages,
-        )
-        
-        # Parse the response
-        result = completion.choices[0].message.content
-        
-        # Try to extract JSON if present
-        if '"Rewritten"' in result:
-            try:
-                # Clean up the response
-                result = result.replace('```json', '').replace('```', '')
-                result_json = json.loads(result)
-                polished_prompt = result_json.get('Rewritten', result)
-            except:
-                polished_prompt = result
-        else:
-            polished_prompt = result
-            
-        polished_prompt = polished_prompt.strip().replace("\n", " ")
-        return polished_prompt
-        
-    except Exception as e:
-        print(f"Error during API call to Hugging Face: {e}")
-        # Fallback to original prompt if enhancement fails
-        return original_prompt
-    
-def next_scene_prompt(original_prompt, img_list):
-    """
-    Rewrites the prompt using a Hugging Face InferenceClient.
-    Supports multiple images via img_list.
-    """
-    # Ensure HF_TOKEN is set
-    api_key = os.environ.get("HF_TOKEN")
-    if not api_key:
-        print("Warning: HF_TOKEN not set. Falling back to original prompt.")
-        return original_prompt
-    prompt = f"{NEXT_SCENE_SYSTEM_PROMPT}"
-    system_prompt = "you are a helpful assistant, you should provide useful answers to users."
-    try:
-        # Initialize the client
-        client = InferenceClient(
-            provider="nebius",
-            api_key=api_key,
-        )
-
-        # Convert list of images to base64 data URLs
-        image_urls = []
-        if img_list is not None:
-            # Ensure img_list is actually a list
-            if not isinstance(img_list, list):
-                img_list = [img_list]
-            
-            for img in img_list:
-                image_url = None
-                # If img is a PIL Image
-                if hasattr(img, 'save'):  # Check if it's a PIL Image
-                    buffered = BytesIO()
-                    img.save(buffered, format="PNG")
-                    img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
-                    image_url = f"data:image/png;base64,{img_base64}"
-                # If img is already a file path (string)
-                elif isinstance(img, str):
-                    with open(img, "rb") as image_file:
-                        img_base64 = base64.b64encode(image_file.read()).decode('utf-8')
-                    image_url = f"data:image/png;base64,{img_base64}"
-                else:
-                    print(f"Warning: Unexpected image type: {type(img)}, skipping...")
-                    continue
-                
-                if image_url:
-                    image_urls.append(image_url)
-
-        # Build the content array with text first, then all images
-        content = [
-            {
-                "type": "text",
-                "text": prompt
-            }
-        ]
-        
-        # Add all images to the content
-        for image_url in image_urls:
-            content.append({
-                "type": "image_url",
-                "image_url": {
-                    "url": image_url
-                }
-            })
-
-        # Format the messages for the chat completions API
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {
-                "role": "user",
-                "content": content
-            }
-        ]
-
-        # Call the API
-        completion = client.chat.completions.create(
-            model="Qwen/Qwen2.5-VL-72B-Instruct",
-            messages=messages,
-        )
-        
-        # Parse the response
-        result = completion.choices[0].message.content
-        
-        # Try to extract JSON if present
-        if '"Rewritten"' in result:
-            try:
-                # Clean up the response
-                result = result.replace('```json', '').replace('```', '')
-                result_json = json.loads(result)
-                polished_prompt = result_json.get('Rewritten', result)
-            except:
-                polished_prompt = result
-        else:
-            polished_prompt = result
-            
-        polished_prompt = polished_prompt.strip().replace("\n", " ")
-        return polished_prompt
-        
-    except Exception as e:
-        print(f"Error during API call to Hugging Face: {e}")
-        # Fallback to original prompt if enhancement fails
-        return original_prompt 
-
-
-def update_history(new_images, history):
-    """Updates the history gallery with the new images."""
-    time.sleep(0.5)  # Small delay to ensure images are ready
-    if history is None:
-        history = []
-    if new_images is not None and len(new_images) > 0:
-        if not isinstance(history, list):
-            history = list(history) if history else []
-        for img in new_images:
-            history.insert(0, img)
-    history = history[:20]  # Keep only last 20 images
-    return history
-
-def use_history_as_input(evt: gr.SelectData):
-    """Sets the selected history image as the new input image."""
-    if evt.value is not None:
-        return gr.update(value=[(evt.value,)])
-    return gr.update()
-    
-def encode_image(pil_image):
-    import io
-    buffered = io.BytesIO()
-    pil_image.save(buffered, format="PNG")
-    return base64.b64encode(buffered.getvalue()).decode("utf-8")
 
 # --- Model Loading ---
 dtype = torch.bfloat16
@@ -388,11 +40,11 @@ pipe = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509",
                                                                                                          device_map='cuda'),torch_dtype=dtype).to(device)
 
 pipe.load_lora_weights(
-        "lovis93/next-scene-qwen-image-lora-2509", 
-        weight_name="next-scene_lora-v2-3000.safetensors", adapter_name="next-scene"
+        "dx8152/Qwen-Edit-2509-Multiple-angles", 
+        weight_name="镜头转换.safetensors", adapter_name="angles"
     )
-pipe.set_adapters(["next-scene"], adapter_weights=[1.])
-pipe.fuse_lora(adapter_names=["next-scene"], lora_scale=1.)
+pipe.set_adapters(["angles"], adapter_weights=[1.])
+pipe.fuse_lora(adapter_names=["angles"], lora_scale=1.)
 pipe.unload_lora_weights()
 
 
@@ -443,7 +95,6 @@ def infer(
     num_inference_steps=4,
     height=None,
     width=None,
-    rewrite_prompt=True,
     num_images_per_prompt=1,
     progress=gr.Progress(track_tqdm=True),
 ):
@@ -478,9 +129,6 @@ def infer(
     print(f"Calling pipeline with prompt: '{prompt}'")
     print(f"Negative Prompt: '{negative_prompt}'")
     print(f"Seed: {seed}, Steps: {num_inference_steps}, Guidance: {true_guidance_scale}, Size: {width}x{height}")
-    if rewrite_prompt and len(pil_images) > 0:
-        prompt = polish_prompt_hf(prompt, pil_images)
-        print(f"Rewritten Prompt: {prompt}")
     
 
     # Generate the image
@@ -590,8 +238,6 @@ with gr.Blocks(css=css) as demo:
                             value=None,
                         )
                         
-                        
-                        rewrite_prompt = gr.Checkbox(label="Rewrite prompt", value=False)
 
         
 
@@ -599,19 +245,8 @@ with gr.Blocks(css=css) as demo:
                 result = gr.Gallery(label="Result", show_label=False, type="pil")
                 with gr.Row():
                     use_output_btn = gr.Button("↗️ Use as input", variant="secondary", size="sm", visible=False)
-                    turn_video_btn = gr.Button("🎬 Turn into Video", variant="secondary", size="sm", visible=False)
-                output_video = gr.Video(label="Generated Video", autoplay=True, visible=False)
 
-                with gr.Row(visible=False):
-                    gr.Markdown("### 📜 History")
-                    clear_history_button = gr.Button("🗑️ Clear History", size="sm", variant="stop")
                 
-                history_gallery = gr.Gallery(
-                    label="Click any image to use as input", 
-                    interactive=False,
-                    show_label=True,
-                    visible=False
-                )
 
         gr.Examples(examples=[
             [["disaster_girl.jpg", "grumpy.png"], "Next Scene: the camera zooms in, showing the cat walking away from the fire"],
@@ -637,14 +272,8 @@ with gr.Blocks(css=css) as demo:
             num_inference_steps,
             height,
             width,
-            rewrite_prompt,
         ],
-        outputs=[result, seed, use_output_btn, turn_video_btn],
-
-    ).then(
-    fn=update_history,
-    inputs=[result, history_gallery],
-    outputs=history_gallery,
+        outputs=[result, seed, use_output_btn],
 
     )
 
@@ -655,33 +284,6 @@ with gr.Blocks(css=css) as demo:
         outputs=[input_images]
     )
 
-    # History gallery event handlers
-    history_gallery.select(
-        fn=use_history_as_input,
-        inputs=None,
-        outputs=[input_images],
-
-    )
-    
-    clear_history_button.click(
-        fn=lambda: [],
-        inputs=None,
-        outputs=history_gallery,
-
-    )
-
-    input_images.change(fn=suggest_next_scene_prompt, inputs=[input_images], outputs=[prompt])
-
-    turn_video_btn.click(
-    fn=lambda: gr.update(visible=True),   
-    inputs=None,
-    outputs=[output_video],
-).then(
-    fn=turn_into_video,                   
-    inputs=[input_images, result, prompt],
-    outputs=[output_video],
-)
-
 
 if __name__ == "__main__":
     demo.launch()