| import gradio as gr |
| import random |
| import os |
| import requests |
| import base64 |
| from PIL import Image |
| from io import BytesIO |
|
|
| HF_TOKEN = os.environ.get("girlToken") |
|
|
| API_BASE = "https://prithivmlmods-qwen-image-edit-2511-loras-fast.hf.space" |
| INFER_URL = f"{API_BASE}/gradio/infer" |
| NAMED_API_URL = f"{API_BASE}/gradio_api/call/v2/infer" |
|
|
| LORA_STYLES = [ |
| 'Multiple-Angles', 'Photo-to-Anime', 'Anime-V2', 'Light-Migration', |
| 'Upscaler', 'Style-Transfer', 'Manga-Tone', 'Anything2Real', |
| 'Fal-Multiple-Angles', 'Polaroid-Photo', 'Unblur-Anything', |
| 'Midnight-Noir-Eyes-Spotlight', 'Hyper-Realistic-Portrait', |
| 'Ultra-Realistic-Portrait', 'Pixar-Inspired-3D', 'Noir-Comic-Book', |
| 'Any-light', 'Studio-DeLight', 'Cinematic-FlatLog', |
| ] |
| MAX_SEED = 2**31 - 1 |
|
|
| def encode_image_file_to_b64_payload(image_path): |
| """ |
| 返回符合API要求的图片Payload对象(list，不是json字符串)。 |
| """ |
| try: |
| with open(image_path, "rb") as f: |
| image_bytes = f.read() |
| |
| try: |
| img = Image.open(BytesIO(image_bytes)) |
| buffered = BytesIO() |
| img.save(buffered, format="JPEG") |
| image_bytes = buffered.getvalue() |
| except Exception: |
| pass |
| im_b64 = base64.b64encode(image_bytes).decode("utf-8") |
| payload = [ |
| { |
| "data": im_b64, |
| "mime_type": "image/jpeg", |
| "orig_name": os.path.basename(image_path), |
| } |
| ] |
| return payload |
| except Exception as e: |
| raise RuntimeError(f"图片编码失败: {e}") |
|
|
| def call_named_infer( |
| images_b64_payload, |
| prompt, |
| lora_adapter, |
| seed, |
| randomize_seed, |
| guidance_scale, |
| steps |
| ): |
| headers = { |
| 'Authorization': f'Bearer {HF_TOKEN}', |
| 'Content-Type': 'application/json' |
| } |
| payload = { |
| "images_b64_json": images_b64_payload, |
| "prompt": prompt, |
| "lora_adapter": lora_adapter, |
| "seed": int(seed), |
| "randomize_seed": bool(randomize_seed), |
| "guidance_scale": float(guidance_scale), |
| "steps": int(steps), |
| } |
| print("准备调用/infer:", {**payload, "images_b64_json": "[payload omitted for brevity]"}) |
| import json |
| |
| resp = requests.post(NAMED_API_URL, data=json.dumps(payload), headers=headers) |
| resp.raise_for_status() |
| job = resp.json() |
| event_id = job.get("event_id") |
| return event_id |
|
|
| def poll_infer(event_id): |
| url = f"{API_BASE}/gradio_api/call/infer/{event_id}" |
| headers = {'Authorization': f'Bearer {HF_TOKEN}'} |
| import time |
| for i in range(60): |
| resp = requests.get(url, headers=headers) |
| try: |
| result = resp.json() |
| except Exception: |
| print(f"[轮询第{i+1}次] 响应无法decode，返回内容：{resp.text[:200]}") |
| time.sleep(2) |
| continue |
| if result.get("status") == "complete": |
| return result.get("data"), result.get("outputs") |
| elif result.get("status") == "error": |
| raise Exception(result.get("error")) |
| time.sleep(2) |
| raise TimeoutError("等候API返回超时") |
|
|
| def infer( |
| image, |
| prompt, |
| lora_adapter, |
| seed, |
| randomize_seed, |
| guidance_scale, |
| steps, |
| progress=gr.Progress(track_tqdm=True), |
| ): |
| if image is None: |
| print("未上传图片") |
| return None, seed |
|
|
| if not os.path.exists(image): |
| print(f"图片路径不存在: {image}") |
| return None, seed |
|
|
| if randomize_seed: |
| seed = random.randint(0, MAX_SEED) |
|
|
| try: |
| images_b64_payload = encode_image_file_to_b64_payload(image) |
| except Exception as e: |
| print(f"[图片 base64编码失败] {e}") |
| return None, seed |
|
|
| try: |
| event_id = call_named_infer( |
| images_b64_payload, |
| prompt, |
| lora_adapter, |
| seed, |
| randomize_seed, |
| guidance_scale, |
| steps |
| ) |
| print("API返回event_id:", event_id) |
| data, outputs = poll_infer(event_id) |
| print("[API 完成] data:", data, "outputs:", outputs) |
| img_out = None |
| seed_used = seed |
| if outputs: |
| if isinstance(outputs, dict): |
| img_out = outputs.get("url") or outputs.get("path") |
| seed_used = outputs.get("seed", seed) |
| elif isinstance(outputs, str) and outputs.startswith("/"): |
| img_out = API_BASE + outputs |
| else: |
| img_out = outputs |
| elif data: |
| if isinstance(data, dict): |
| img_out = data.get("url") or data.get("path") |
| seed_used = data.get("seed", seed) |
| elif isinstance(data, str) and data.startswith("/"): |
| img_out = API_BASE + data |
| else: |
| img_out = data |
| if img_out and isinstance(img_out, str) and not img_out.startswith("http"): |
| img_out = API_BASE + img_out |
| return img_out, int(seed_used) |
| except Exception as e: |
| import traceback |
| traceback.print_exc() |
| print(f"[API 调用异常] {e}") |
| return None, seed |
|
|
| css = """ |
| #col-container { |
| margin: 0 auto; |
| max-width: 640px; |
| } |
| """ |
|
|
| with gr.Blocks(css=css) as demo: |
| with gr.Column(elem_id="col-container"): |
| gr.Markdown("# 图像编辑 Demo\n基于 prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast (新版API)") |
|
|
| image = gr.Image( |
| label="上传图片", |
| sources=["upload"], |
| type="filepath", |
| ) |
|
|
| prompt = gr.Text( |
| label="编辑描述（Prompt）", |
| placeholder="请输入图片编辑描述...", |
| ) |
|
|
| lora_adapter = gr.Dropdown( |
| label="编辑风格（Style）", |
| choices=LORA_STYLES, |
| value="Photo-to-Anime" |
| ) |
|
|
| run_button = gr.Button("执行编辑", variant="primary") |
|
|
| result = gr.Image(label="结果图片", show_label=True) |
|
|
| with gr.Accordion("高级设置", open=False): |
| seed = gr.Slider( |
| label="随机种子", |
| minimum=0, |
| maximum=MAX_SEED, |
| step=1, |
| value=0, |
| ) |
| randomize_seed = gr.Checkbox(label="随机化种子", value=True) |
| guidance_scale = gr.Slider( |
| label="引导强度 (Guidance Scale)", |
| minimum=1.0, |
| maximum=10.0, |
| step=0.1, |
| value=1.0, |
| ) |
| steps = gr.Slider( |
| label="推理步数 (Steps)", |
| minimum=1, |
| maximum=50, |
| step=1, |
| value=4, |
| ) |
|
|
| gr.on( |
| triggers=[run_button.click, prompt.submit], |
| fn=infer, |
| inputs=[image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps], |
| outputs=[result, seed], |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch(ssr_mode=False, share=True) |
