Delete app_fast.py

app_fast.py

@@ -1,198 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-import torch
-import spaces
-from PIL import Image
-from diffusers import QwenImageEditPipeline
-import os
-import base64
-import json
-from huggingface_hub import InferenceClient
-
-def get_caption_language(prompt):
-    """Detects if the prompt contains Chinese characters."""
-    ranges = [
-        ('\u4e00', '\u9fff'),  # CJK Unified Ideographs
-    ]
-    for char in prompt:
-        if any(start <= char <= end for start, end in ranges):
-            return 'zh'
-    return 'en'
-
-def polish_prompt(original_prompt, system_prompt):
-    """
-    Rewrites the prompt using a Hugging Face InferenceClient.
-    """
-    api_key = os.environ.get("HF_TOKEN")
-    if not api_key:
-        raise EnvironmentError("HF_TOKEN is not set. Please set it in your environment.")
-    client = InferenceClient(
-        provider="cerebras",
-        api_key=api_key,
-    )
-    messages = [
-        {"role": "system", "content": system_prompt},
-        {"role": "user", "content": original_prompt}
-    ]
-    try:
-        completion = client.chat.completions.create(
-            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
-            messages=messages,
-            max_tokens=2000,
-        )
-        polished_prompt = completion.choices[0].message.content
-        polished_prompt = polished_prompt.strip().replace("\n", " ")
-        return polished_prompt
-    except Exception as e:
-        print(f"Error during Hugging Face API call: {e}")
-        return original_prompt
-
-SYSTEM_PROMPT_EDIT = '''
-# Edit Instruction Rewriter
-You are a professional edit instruction rewriter. Your task is to generate a precise, concise, and visually achievable instruction based on the user's intent and the input image.
-## 1. General Principles
-- Keep the rewritten instruction **concise** and clear.
-- Avoid contradictions, vagueness, or unachievable instructions.
-- Maintain the core logic of the original instruction; only enhance clarity and feasibility.
-- Ensure new added elements or modifications align with the image's original context and art style.
-## 2. Task Types
-### Add, Delete, Replace:
-- When the input is detailed, only refine grammar and clarity.
-- For vague instructions, infer minimal but sufficient details.
-- For replacement, use the format: `"Replace X with Y"`.
-### Text Editing (e.g., text replacement):
-- Enclose text content in quotes, e.g., `Replace "abc" with "xyz"`.
-- Preserving the original structure and language—**do not translate** or alter style.
-### Human Editing (e.g., change a person’s face/hair):
-- Preserve core visual identity (gender, ethnic features).
-- Describe expressions in subtle and natural terms.
-- Maintain key clothing or styling details unless explicitly replaced.
-### Style Transformation:
-- If a style is specified, e.g., `Disco style`, rewrite it to encapsulate the essential visual traits.
-- Use a fixed template for **coloring/restoration**:  
-  `"Restore old photograph, remove scratches, reduce noise, enhance details, high resolution, realistic, natural skin tones, clear facial features, no distortion, vintage photo restoration"`  
-  if applicable.
-## 4. Output Format
-Please provide the rewritten instruction in a clean `json` format as:
-{
-  "Rewritten": "..."
-}
-'''
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=dtype).to(device)
-# Load LoRA weights for acceleration
-pipe.load_lora_weights(
-    "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.1.safetensors"
-)
-pipe.fuse_lora()
-
-@spaces.GPU(duration=60)
-def infer(
-    image,
-    prompt,
-    seed=42,
-    randomize_seed=False,
-    true_guidance_scale=1.0,
-    num_inference_steps=8,
-    rewrite_prompt=False,
-    num_images_per_prompt=1,
-    progress=gr.Progress(track_tqdm=True),
-):
-    """
-    Uses Qwen-Image-Edit with optional prompt rewriting before execution.
-    """
-    negative_prompt = " "
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-    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}")
-    if rewrite_prompt:
-        lang = get_caption_language(prompt)
-        system_prompt = SYSTEM_PROMPT_EDIT
-        polished_prompt = polish_prompt(prompt, system_prompt)
-        print(f"Rewritten Prompt: {polished_prompt}")
-        prompt = polished_prompt
-    edited_images = pipe(
-        image,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        true_cfg_scale=true_guidance_scale,
-        num_images_per_prompt=num_images_per_prompt,
-    ).images
-    return edited_images, seed
-
-MAX_SEED = np.iinfo(np.int32).max
-examples = [
-    "Replace the cat with a friendly golden retriever. Make it look happier, and add more background details.",
-    "Add text 'Qwen - AI for image editing' in Chinese at the bottom center with a small shadow.",
-    "Change the style to 1970s vintage, add old photo effect, restore any scratches on the wall or window.",
-    "Remove the blue sky and replace it with a dark night cityscape.",
-    """Replace "Qwen" with "通义" in the Image. Ensure Chinese font is used for "通义" and position it to the top left with a light heading-style font."""
-]
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Qwen-Image-Edit with Prompt Enhancement and Fast Inference")
-    gr.Markdown("Try editing images with multi-modal instruction polishing.")
-    with gr.Column():
-        input_image = gr.Image(label="Input Image", type="pil")
-        prompt = gr.Text(label="Edit Instruction", placeholder="e.g. Add a dog to the right side.")
-        run_button = gr.Button("Edit", variant="primary")
-        result = gr.Gallery(label="Output Images", show_label=False)
-    with gr.Accordion("Advanced Settings", open=False):
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0
-        )
-        randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-        with gr.Row():
-            true_guidance_scale = gr.Slider(
-                label="True Guidance Scale",
-                minimum=1.0,
-                maximum=5.0,
-                step=0.1,
-                value=4.0
-            )
-            num_inference_steps = gr.Slider(
-                label="Inference Steps (Fast 8-step mode)",
-                minimum=4,
-                maximum=8,
-                step=1,
-                value=8
-            )
-            num_images_per_prompt = gr.Slider(
-                label="Images per Prompt",
-                minimum=1,
-                maximum=4,
-                step=1,
-                value=1
-            )
-            rewrite_prompt = gr.Checkbox(label="Use Prompt Rewriter", value=False, visible=True)
-    
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            input_image,
-            prompt,
-            seed,
-            randomize_seed,
-            true_guidance_scale,
-            num_inference_steps,
-            rewrite_prompt,
-            num_images_per_prompt
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()