lora retrieval

lora/test_merger.py

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+from diffsynth import FluxImagePipeline, ModelManager, load_state_dict
+from diffsynth.models.lora import FluxLoRAConverter
+from diffsynth.pipelines.flux_image import lets_dance_flux
+from lora.dataset import LoraDataset
+from lora.merger import LoraPatcher
+from lora.utils import load_lora
+import torch, os
+from accelerate import Accelerator, DistributedDataParallelKwargs
+from tqdm import tqdm
+
+
+model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cuda", model_id_list=["FLUX.1-dev"])
+pipe = FluxImagePipeline.from_model_manager(model_manager)
+pipe.enable_auto_lora()
+
+lora_patcher = LoraPatcher().to(dtype=torch.bfloat16, device="cuda")
+lora_patcher.load_state_dict(load_state_dict("models/lora_merger/epoch-3.safetensors"))
+
+dataset = LoraDataset("data/lora/models", "data/lora/lora_dataset_1000.csv", steps_per_epoch=800, loras_per_item=4)
+
+for seed in range(100):
+    batch = dataset[0]
+    num_lora = torch.randint(1, len(batch), (1,))[0]
+    lora_state_dicts = [
+        FluxLoRAConverter.align_to_diffsynth_format(load_lora(batch[i]["model_file"], device="cuda")) for i in range(num_lora)
+    ]
+    image = pipe(
+        prompt=batch[0]["text"],
+        seed=seed,
+    )
+    image.save(f"data/lora/lora_outputs/image_{seed}_nolora.jpg")
+    for i in range(num_lora):
+        image = pipe(
+            prompt=batch[0]["text"],
+            lora_state_dicts=[lora_state_dicts[i]], 
+            lora_patcher=lora_patcher,
+            seed=seed,
+        )
+        image.save(f"data/lora/lora_outputs/image_{seed}_{i}.jpg")
+    image = pipe(
+        prompt=batch[0]["text"],
+        lora_state_dicts=lora_state_dicts, 
+        lora_patcher=lora_patcher,
+        seed=seed,
+    )
+    image.save(f"data/lora/lora_outputs/image_{seed}_merger.jpg")