debug dop lora

examples/text_to_image_sdxl_dpo.py

@@ -0,0 +1,46 @@
+# Need `pip install -U peft transformers`
+
+from diffusers import DiffusionPipeline
+from diffusers.utils import make_image_grid
+import torch
+
+pipe = DiffusionPipeline.from_pretrained(
+    "/share_nfs/hf_models/sd-turbo",  # SD Turbo is a destilled version of Stable Diffusion 2.1
+    #"stabilityai/sd-turbo",  # SD Turbo is a destilled version of Stable Diffusion 2.1
+    # "stabilityai/stable-diffusion-2-1", # for the original stable diffusion 2.1 model
+    torch_dtype=torch.float16, variant="fp16"
+)
+pipe.to("cuda")
+
+from onediff.infer_compiler import oneflow_compile
+
+pipe.unet = oneflow_compile(pipe.unet, options={"all_dynamic": True})
+pipe.vae.encoder = oneflow_compile(pipe.vae.encoder)
+pipe.vae.decoder = oneflow_compile(pipe.vae.decoder)
+
+pipe.load_lora_weights("radames/sd-21-DPO-LoRA", adapter_name="dpo-lora-sd21")
+pipe.set_adapters(["dpo-lora-sd21"], adapter_weights=[1.0]) # you can play with adapter_weights to increase the effect of the LoRA model
+seed = 123123
+prompt = "portrait headshot professional of elon musk"
+negative_prompt = "3d render, cartoon, drawing, art, low light"
+generator = torch.Generator().manual_seed(seed)
+
+def run_once(dynamic_guidance_scale):
+    for i in range(2):
+        images = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=512,
+            height=512,
+            num_inference_steps=2,
+            generator=generator,
+            guidance_scale=dynamic_guidance_scale,
+            num_images_per_prompt=4
+        ).images
+        make_image_grid(images, 1, 4)
+
+run_once(1.0)
+# Adjust guidance_scale
+# Will raise error
+run_once(1.5)
+