|
|
| try:
|
| from transformers import Qwen2_5_VLForConditionalGeneration
|
| print("✅ 成功导入！")
|
| except Exception as e:
|
| print("❌ 导入失败：", e)
|
| import traceback
|
| traceback.print_exc()
|
|
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-Image-Edit",trust_remote_code=True)
|
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-Image-Edit",trust_remote_code=True)
|
| import os
|
| from PIL import Image
|
| import torch
|
|
|
| from diffusers import QwenImageEditPipeline
|
|
|
| pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit")
|
| print("pipeline loaded")
|
| pipeline.to(torch.bfloat16)
|
| pipeline.to("cuda")
|
| pipeline.set_progress_bar_config(disable=None)
|
|
|
| image = Image.open("./input.png").convert("RGB")
|
| prompt = "Change the rabbit's color to purple, with a flash light background."
|
|
|
|
|
| inputs = {
|
| "image": image,
|
| "prompt": prompt,
|
| "generator": torch.manual_seed(0),
|
| "true_cfg_scale": 4.0,
|
| "negative_prompt": " ",
|
| "num_inference_steps": 50,
|
| }
|
|
|
| with torch.inference_mode():
|
| output = pipeline(**inputs)
|
| output_image = output.images[0]
|
| output_image.save("output_image_edit.png")
|
| print("image saved at", os.path.abspath("output_image_edit.png")) |
