RuntimeError: Calculated padded input size per channel: (4 x 2). Kernel size: (3 x 3). Kernel size can't be greater than actual input size

[luliloveshe918]

The error when calling txt2img is as follows:， Is this a bug?

API error: POST: http://192.168.1.110:7860/sdapi/v1/txt2img {'error': 'RuntimeError', 'detail': '', 'body': '', 'errors': "Calculated padded input size per channel: (4 x 2). Kernel size: (3 x 3). Kernel size can't be greater than actual input size"}
Traceback (most recent call last):
File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\anyio\streams\memory.py", line 98, in receive
return self.receive_nowait()
File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\anyio\streams\memory.py", line 93, in receive_nowait
raise WouldBlock
anyio.WouldBlock

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\base.py", line 78, in call_next
    message = await recv_stream.receive()
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\anyio\streams\memory.py", line 118, in receive
    raise EndOfStream
anyio.EndOfStream

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "D:\SD\stable-diffusion-webui\modules\api\api.py", line 152, in exception_handling
    return await call_next(request)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\base.py", line 84, in call_next
    raise app_exc
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\base.py", line 70, in coro
    await self.app(scope, receive_or_disconnect, send_no_error)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\base.py", line 108, in __call__
    response = await self.dispatch_func(request, call_next)
  File "D:\SD\stable-diffusion-webui\modules\api\api.py", line 116, in log_and_time
    res: Response = await call_next(req)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\base.py", line 84, in call_next
    raise app_exc
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\base.py", line 70, in coro
    await self.app(scope, receive_or_disconnect, send_no_error)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\cors.py", line 84, in __call__
    await self.app(scope, receive, send)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\gzip.py", line 24, in __call__
    await responder(scope, receive, send)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\gzip.py", line 44, in __call__
    await self.app(scope, receive, self.send_with_gzip)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\exceptions.py", line 79, in __call__
    raise exc
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\middleware\exceptions.py", line 68, in __call__
    await self.app(scope, receive, sender)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\fastapi\middleware\asyncexitstack.py", line 21, in __call__
    raise e
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\fastapi\middleware\asyncexitstack.py", line 18, in __call__
    await self.app(scope, receive, send)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\routing.py", line 718, in __call__
    await route.handle(scope, receive, send)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\routing.py", line 276, in handle
    await self.app(scope, receive, send)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\routing.py", line 66, in app
    response = await func(request)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\fastapi\routing.py", line 237, in app
    raw_response = await run_endpoint_function(
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\fastapi\routing.py", line 165, in run_endpoint_function
    return await run_in_threadpool(dependant.call, **values)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\starlette\concurrency.py", line 41, in run_in_threadpool
    return await anyio.to_thread.run_sync(func, *args)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\anyio\to_thread.py", line 33, in run_sync
    return await get_asynclib().run_sync_in_worker_thread(
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\anyio\_backends\_asyncio.py", line 877, in run_sync_in_worker_thread
    return await future
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\anyio\_backends\_asyncio.py", line 807, in run
    result = context.run(func, *args)
  File "D:\SD\stable-diffusion-webui\modules\api\api.py", line 343, in text2imgapi
    processed = process_images(p)
  File "D:\SD\stable-diffusion-webui\modules\processing.py", line 677, in process_images
    res = process_images_inner(p)
  File "D:\SD\stable-diffusion-webui\modules\processing.py", line 794, in process_images_inner
    samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
  File "D:\SD\stable-diffusion-webui\modules\processing.py", line 1054, in sample
    samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
  File "D:\SD\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 464, in sample
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "D:\SD\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 303, in launch_sampling
    return func()
  File "D:\SD\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 464, in <lambda>
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 128, in sample_euler
    denoised = model(x, sigma_hat * s_in, **extra_args)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 183, in forward
    x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 112, in forward
    eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
  File "D:\SD\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
    return self.inner_model.apply_model(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\modules\sd_models_xl.py", line 37, in apply_model
    return self.model(x, t, cond)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\modules\sd_hijack_utils.py", line 17, in <lambda>
    setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
  File "D:\SD\stable-diffusion-webui\modules\sd_hijack_utils.py", line 28, in __call__
    return self.__orig_func(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\wrappers.py", line 28, in forward
    return self.diffusion_model(
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 993, in forward
    h = module(h, emb, context)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 102, in forward
    x = layer(x)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "D:\SD\stable-diffusion-webui\extensions-builtin\Lora\networks.py", line 376, in network_Conv2d_forward
    return torch.nn.Conv2d_forward_before_network(self, input)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "D:\SD\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
    return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Calculated padded input size per channel: (4 x 2). Kernel size: (3 x 3). Kernel size can't be greater than actual input size