train_controlnet

from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch

base_model_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
controlnet_path = "./output/checkpoint-12500/controlnet/"

controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model_path, controlnet=controlnet, torch_dtype=torch.float16
)

# speed up diffusion process with faster scheduler and memory optimization
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
# remove following line if xformers is not installed or when using Torch 2.0.
pipe.enable_xformers_memory_efficient_attention()
# memory optimization.
pipe.enable_model_cpu_offload()

control_image = load_image("./conditioning_image_1.png")
prompt = "pale golden rod circle with old lace background"

# generate image
generator = torch.manual_seed(0)
image = pipe(
prompt, num_inference_steps=20, generator=generator, image=control_image
).images[0]
image.save("./output.png")

Error when using the official inference script
RuntimeError: Data processing error: CAS service error : Reqwest Error: HTTP status client error (401 Unauthorized), domain: https://cas-server.xethub.hf.co/reconstructions/3e6084a7831fd989ab9e7a6a88745fce55823b69b6ea6832ff6c5cc08fee958f

Reproduce the reported script, starting at ControlNetModel.from_pretrained and StableDiffusionControlNetPipeline.from_pretrained, and inspect the 401 CAS error while loading the model. Confirm the relevant package and environment details, then determine whether the official inference path can complete successfully and document the supported resolution.

Written by the indexing model from the issue text.