Upload check_logits_with_serialization_peft_lora.py

check_logits_with_serialization_peft_lora.py

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+# SDXL: 0.613, 0.5566, 0.54, 0.4162, 0.4042, 0.4596, 0.5374, 0.5286, 0.5038
+# SD: 0.5396, 0.5707, 0.477, 0.4665, 0.5419, 0.4594, 0.4857, 0.4741, 0.4804
+
+from diffusers import DiffusionPipeline
+from huggingface_hub import upload_folder
+from peft import LoraConfig
+import argparse
+import torch
+
+from peft.utils import get_peft_model_state_dict
+from diffusers.utils import convert_state_dict_to_diffusers
+from diffusers.loaders import StableDiffusionXLLoraLoaderMixin, LoraLoaderMixin
+from huggingface_hub import create_repo, upload_folder
+
+
+mapping = {
+    "hf-internal-testing/tiny-sd-pipe": "hf-internal-testing/tiny-sd-lora-peft",
+    "hf-internal-testing/tiny-sdxl-pipe": "hf-internal-testing/tiny-sdxl-lora-peft",
+}
+
+
+def load_pipeline(pipeline_id):
+    pipe = DiffusionPipeline.from_pretrained(pipeline_id)
+    return pipe
+
+
+def get_lora_config():
+    rank = 4
+
+    torch.manual_seed(0)
+    text_lora_config = LoraConfig(
+        r=rank,
+        lora_alpha=rank,
+        target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
+        init_lora_weights=False,
+    )
+
+    torch.manual_seed(0)
+    unet_lora_config = LoraConfig(
+        r=rank,
+        lora_alpha=rank,
+        target_modules=["to_q", "to_k", "to_v", "to_out.0"],
+        init_lora_weights=False,
+    )
+    return text_lora_config, unet_lora_config
+
+
+def get_dummy_inputs():
+    pipeline_inputs = {
+        "prompt": "A painting of a squirrel eating a burger",
+        "num_inference_steps": 2,
+        "guidance_scale": 6.0,
+        "output_type": "np",
+        "generator": torch.manual_seed(0),
+    }
+    return pipeline_inputs
+
+
+def run_inference(args):
+    has_two_text_encoders = False
+    pipe = load_pipeline(pipeline_id=args.pipeline_id)
+    text_lora_config, unet_lora_config = get_lora_config()
+
+    pipe.text_encoder.add_adapter(text_lora_config)
+    pipe.unet.add_adapter(unet_lora_config)
+    if hasattr(pipe, "text_encoder_2"):
+        pipe.text_encoder_2.add_adapter(text_lora_config)
+        has_two_text_encoders = True
+
+    inputs = get_dummy_inputs()
+    outputs = pipe(**inputs).images
+    predicted_slice = outputs[0, -3:, -3:, -1].flatten().tolist()
+
+    print(", ".join([str(round(x, 4)) for x in predicted_slice]))
+
+    if args.push_to_hub:
+        text_encoder_state_dict = convert_state_dict_to_diffusers(
+            get_peft_model_state_dict(pipe.text_encoder)
+        )
+        unet_state_dict = convert_state_dict_to_diffusers(
+            get_peft_model_state_dict(pipe.unet)
+        )
+        if has_two_text_encoders:
+            text_encoder_2_state_dict = convert_state_dict_to_diffusers(
+                get_peft_model_state_dict(pipe.text_encoder_2)
+            )
+
+        serialization_cls = (
+            StableDiffusionXLLoraLoaderMixin
+            if has_two_text_encoders
+            else LoraLoaderMixin
+        )
+        output_dir = mapping[args.pipeline_id].split("/")[-1]
+
+        if not has_two_text_encoders:
+            serialization_cls.save_lora_weights(
+                save_directory=output_dir,
+                unet_lora_layers=unet_state_dict,
+                text_encoder_lora_layers=text_encoder_state_dict,
+            )
+        else:
+            serialization_cls.save_lora_weights(
+                save_directory=output_dir,
+                unet_lora_layers=unet_state_dict,
+                text_encoder_lora_layers=text_encoder_state_dict,
+                text_encoder_2_lora_layers=text_encoder_2_state_dict,
+            )
+
+        repo_id = create_repo(repo_id=mapping[args.pipeline_id], exist_ok=True).repo_id
+        upload_folder(repo_id=repo_id, folder_path=output_dir)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--pipeline_id",
+        type=str,
+        default="hf-internal-testing/tiny-sd-pipe",
+        choices=[
+            "hf-internal-testing/tiny-sd-pipe",
+            "hf-internal-testing/tiny-sdxl-pipe",
+        ],
+    )
+    parser.add_argument("--push_to_hub", action="store_true")
+    args = parser.parse_args()
+
+    run_inference(args)