support training for eligen and nexusgen

examples/flux/model_training/validate_lora/FLUX.1-dev-EliGen.py

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+import torch
+from PIL import Image
+from diffsynth.pipelines.flux_image_new import FluxImagePipeline, ModelConfig
+
+pipe = FluxImagePipeline.from_pretrained(
+    torch_dtype=torch.bfloat16,
+    device="cuda",
+    model_configs=[
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="flux1-dev.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder/model.safetensors"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="text_encoder_2/"),
+        ModelConfig(model_id="black-forest-labs/FLUX.1-dev", origin_file_pattern="ae.safetensors"),
+    ],
+)
+
+pipe.load_lora(pipe.dit, "models/train/FLUX.1-dev-EliGen_lora/epoch-4.safetensors", alpha=1)
+
+entity_prompts = ["A beautiful girl", "sign 'Entity Control'", "shorts", "shirt"]
+global_prompt = "A beautiful girl wearing shirt and shorts in the street,  holding a sign 'Entity Control'"
+masks = [Image.open(f"data/example_image_dataset/eligen/{i}.png").convert('RGB') for i in range(len(entity_prompts))]
+# generate image
+image = pipe(
+    prompt=global_prompt,
+    cfg_scale=1.0,
+    num_inference_steps=50,
+    embedded_guidance=3.5,
+    seed=42,
+    height=1024,
+    width=1024,
+    eligen_entity_prompts=entity_prompts,
+    eligen_entity_masks=masks,
+)
+image.save(f"EliGen_lora.png")