Automatic1111, txt2img generation, I am trying to use 150 text-length for the positive prompt, and 75 text-length for the negative prompt. It works successfully when both positive and negative text-length are 75, it fails if positive is 150 with the following error:
RuntimeError: The expanded size of the tensor (1) must match the existing size (2) at non-singleton dimension 0. Target sizes: [1, 4, 128, 128]. Tensor sizes: [2, 4, 128, 128]
I have set up multiple profiles trying to get this resolved with no success. I currently have the following profiles for Available TensorRT Engine Profiles for the checkpoint I am using:
If I reduce positive to 75 text-length, but make negative 150 text-length, it also fails.
If I add filler words so I have 150 text-length for the positive prompt and negative prompt then it works successfully.
My understanding is that the Profile 0 should support 150 positive and 75 negative, but this does not work in practice. Here is the full console output when trying to generate an image with this mix of text-length 150 for positive and 75 for negative:
0%| | 0/20 [00:00<?, ?it/s][E] 3: [executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046, condition: profileMinDims.d[i] <= dimensions.d[i]. Supplied binding dimension [1,4,128,128] for bindings[0] exceed min ~ max range at index 0, maximum dimension in profile is 2, minimum dimension in profile is 2, but supplied dimension is 1.
)
[E] 3: [executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046, condition: profileMinDims.d[i] <= dimensions.d[i]. Supplied binding dimension [1] for bindings[1] exceed min ~ max range at index 0, maximum dimension in profile is 2, minimum dimension in profile is 2, but supplied dimension is 1.
)
[E] 3: [executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046, condition: profileMinDims.d[i] <= dimensions.d[i]. Supplied binding dimension [1,154,2048] for bindings[2] exceed min ~ max range at index 0, maximum dimension in profile is 2, minimum dimension in profile is 2, but supplied dimension is 1.
)
[E] 3: [executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046] Error Code 3: API Usage Error (Parameter check failed at: runtime/api/executionContext.cpp::nvinfer1::rt::ExecutionContext::validateInputBindings::2046, condition: profileMinDims.d[i] <= dimensions.d[i]. Supplied binding dimension [1,2816] for bindings[3] exceed min ~ max range at index 0, maximum dimension in profile is 2, minimum dimension in profile is 2, but supplied dimension is 1.
)
0%| | 0/20 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(xjdn1sa1n2skykw)', <gradio.routes.Request object at 0x000001BC400FC6D0>, 'PTA_1\ntest words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, \n <lora:PTA_1-26:1>', 'test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words, sample words, positive prompt, generate image, test words', [], 20, 'DPM++ 2M Karras', 1, 1, 5.5, 1024, 1024, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', '', '', [], 0, False, '', 0.8, 343205269, False, -1, 0, 0, 0, UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', inpaint_crop_input_image=False, hr_option='Both', save_detected_map=True, advanced_weighting=None), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', inpaint_crop_input_image=False, hr_option='Both', save_detected_map=True, advanced_weighting=None), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', inpaint_crop_input_image=False, hr_option='Both', save_detected_map=True, advanced_weighting=None), False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False, None, None, False, None, None, False, None, None, False, 50) {}
Traceback (most recent call last):
File "C:\Users\petee\stable-diffusion-webui\modules\call_queue.py", line 57, in f
res = list(func(*args, **kwargs))
File "C:\Users\petee\stable-diffusion-webui\modules\call_queue.py", line 36, in f
res = func(*args, **kwargs)
File "C:\Users\petee\stable-diffusion-webui\modules\txt2img.py", line 110, in txt2img
processed = processing.process_images(p)
File "C:\Users\petee\stable-diffusion-webui\extensions\sd-webui-prompt-history\lib_history\image_process_hijacker.py", line 21, in process_images
res = original_function(p)
File "C:\Users\petee\stable-diffusion-webui\modules\processing.py", line 785, in process_images
res = process_images_inner(p)
File "C:\Users\petee\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 59, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "C:\Users\petee\stable-diffusion-webui\modules\processing.py", line 921, 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 "C:\Users\petee\stable-diffusion-webui\modules\processing.py", line 1257, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "C:\Users\petee\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 234, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "C:\Users\petee\stable-diffusion-webui\modules\sd_samplers_common.py", line 261, in launch_sampling
return func()
File "C:\Users\petee\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 234, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "C:\Users\petee\stable-diffusion-webui\venv\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\Users\petee\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "C:\Users\petee\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\petee\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\petee\stable-diffusion-webui\modules\sd_samplers_cfg_denoiser.py", line 256, in forward
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
RuntimeError: The expanded size of the tensor (1) must match the existing size (2) at non-singleton dimension 0. Target sizes: [1, 4, 128, 128]. Tensor sizes: [2, 4, 128, 128]
---
Is this a bug? How do I get TensorRT to process this mix of positive and negative prompt text-length?
Automatic1111, txt2img generation, I am trying to use 150 text-length for the positive prompt, and 75 text-length for the negative prompt. It works successfully when both positive and negative text-length are 75, it fails if positive is 150 with the following error:
RuntimeError: The expanded size of the tensor (1) must match the existing size (2) at non-singleton dimension 0. Target sizes: [1, 4, 128, 128]. Tensor sizes: [2, 4, 128, 128]I have set up multiple profiles trying to get this resolved with no success. I currently have the following profiles for Available TensorRT Engine Profiles for the checkpoint I am using:
Profile 0
| Min | Opt | Max -- | -- | -- | -- Height | 1024 | 1024 | 1024 Width | 1024 | 1024 | 1024 Batch Size | 1 | 1 | 1 Text-length | 75 | 150 | 225Profile 1
| Min | Opt | Max -- | -- | -- | -- Height | 1024 | 1024 | 1024 Width | 1024 | 1024 | 1024 Batch Size | 1 | 1 | 1 Text-length | 150 | 150 | 150Profile 2
| Min | Opt | Max -- | -- | -- | -- Height | 1024 | 1024 | 1024 Width | 1024 | 1024 | 1024 Batch Size | 1 | 1 | 1 Text-length | 75 | 75 | 75Profile 3
| Min | Opt | Max -- | -- | -- | -- Height | 1024 | 1024 | 1024 Width | 1024 | 1024 | 1024 Batch Size | 1 | 1 | 1 Text-length | 225 | 225 | 225If I reduce positive to 75 text-length, but make negative 150 text-length, it also fails.
If I add filler words so I have 150 text-length for the positive prompt and negative prompt then it works successfully.
My understanding is that the Profile 0 should support 150 positive and 75 negative, but this does not work in practice. Here is the full console output when trying to generate an image with this mix of text-length 150 for positive and 75 for negative:
Is this a bug? How do I get TensorRT to process this mix of positive and negative prompt text-length?