feat&fix(diffusers): add QwenImage lora finetune, new models and pipes and fix bugs

docs/diffusers/api/pipelines/qwenimage.md

@@ -30,7 +30,63 @@ Qwen-Image comes in the following variants:
 !!! tip
 
     Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
-    In addition, the default version of installed `transformers` in `mindone` is `4.50.0`, but `transformers==4.52.1` is required for Qwen-Image. Please using `pip install transformers==4.52.1` to upgrade, if you want to try related Qwen-Image pipelines.
+
+## LoRA for faster inference
+
+Use a LoRA from `lightx2v/Qwen-Image-Lightning` to speed up inference by reducing the
+number of steps. Refer to the code snippet below:
+
+```py
+from mindone.diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+import mindspore
+import math
+
+scheduler_config = {
+    "base_image_seq_len": 256,
+    "base_shift": math.log(3),  # We use shift=3 in distillation
+    "invert_sigmas": False,
+    "max_image_seq_len": 8192,
+    "max_shift": math.log(3),  # We use shift=3 in distillation
+    "num_train_timesteps": 1000,
+    "shift": 1.0,
+    "shift_terminal": None,  # set shift_terminal to None
+    "stochastic_sampling": False,
+    "time_shift_type": "exponential",
+    "use_beta_sigmas": False,
+    "use_dynamic_shifting": True,
+    "use_exponential_sigmas": False,
+    "use_karras_sigmas": False,
+}
+scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
+pipe = DiffusionPipeline.from_pretrained(
+    "Qwen/Qwen-Image", scheduler=scheduler, mindspore_dtype=mindspore.bfloat16
+)
+pipe.load_lora_weights(
+    "Qwen/lightx2v/Qwen-Image-Lightning",
+    weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors",
+    adapter_name="qwenimage-lora"
+)
+pipe.fuse_lora()
+pipe.unload_lora_weights()
+
+prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition."
+negative_prompt = " "
+image = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    width=1024,
+    height=1024,
+    num_inference_steps=8,
+    true_cfg_scale=1.0,
+    generator=None,
+)[0][0]
+image.save("lora_pic/qwen_fewsteps_lora.png")
+```
+
+!!! tip
+
+    The `guidance_scale` parameter in the pipeline is there to support future guidance-distilled models when they come up.
+    Note that passing `guidance_scale` to the pipeline is ineffective. To enable classifier-free guidance, please pass `true_cfg_scale` and `negative_prompt` (even an empty negative prompt like " ") should enable classifier-free guidance computations.
 
 
 ::: mindone.diffusers.QwenImagePipeline