RuntimeError: Input type (c10::Half) and bias type (float) should be the same #7
Description
It is ok for use to generate images for the first time. However, it will raise the following error if we generate images for second time.
Traceback (most recent call last):
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/gradio/routes.py", line 321, in run_predict
output = await app.blocks.process_api(
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/gradio/blocks.py", line 1006, in process_api
result = await self.call_function(fn_index, inputs, iterator, request)
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/gradio/blocks.py", line 847, in call_function
prediction = await anyio.to_thread.run_sync(
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/anyio/to_thread.py", line 31, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/anyio/_backends/_asyncio.py", line 937, in run_sync_in_worker_thread
return await future
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/anyio/_backends/_asyncio.py", line 867, in run
result = context.run(func, *args)
File "app.py", line 123, in infer
images = pipe_refiner(prompt=prompt, negative_prompt=negative, image=images, num_inference_steps=steps, strength=refiner_strength, generator=g).images
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py", line 998, in __call__
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/diffusers/utils/accelerate_utils.py", line 46, in wrapper
return method(self, *args, **kwargs)
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/diffusers/models/autoencoder_kl.py", line 270, in decode
decoded = self._decode(z).sample
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/diffusers/models/autoencoder_kl.py", line 256, in _decode
z = self.post_quant_conv(z)
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 463, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/docker/software/anaconda3/envs/r3d/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Input type (c10::Half) and bias type (float) should be the same