Allow for us to input desired output resolution

[KAmaia]

Could we get some parameters that allow us to designate the desired output resolution. I am attempting to use SD to generate super ultrawide desktop backgrounds, and it would be nice to have this feature

[benedlore]

I used the original arguments and I can control the size. For example, I add "--H 704" and the result is a more portrait aspect ratio, 1024x1408

[KAmaia]

Those arguments seem to get passed to txt2img not txt2imghd. Trying to generate a 7680x1440 image results in the script exhausting all available vram and dying:

Traceback (most recent call last): File "scripts/txt2imgHD.py", line 549, in <module> main() File "scripts/txt2imgHD.py", line 365, in main text2img2(opt) File "scripts/txt2imgHD.py", line 443, in text2img2 samples_ddim, _ = sampler.sample(S=opt.steps, File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "c:\stable-diffusion\ldm\models\diffusion\plms.py", line 97, in sample samples, intermediates = self.plms_sampling(conditioning, size, File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "c:\stable-diffusion\ldm\models\diffusion\plms.py", line 152, in plms_sampling outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "c:\stable-diffusion\ldm\models\diffusion\plms.py", line 218, in p_sample_plms e_t = get_model_output(x, t) File "c:\stable-diffusion\ldm\models\diffusion\plms.py", line 185, in get_model_output e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) File "c:\stable-diffusion\ldm\models\diffusion\ddpm.py", line 987, in apply_model x_recon = self.model(x_noisy, t, **cond) File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "c:\stable-diffusion\ldm\models\diffusion\ddpm.py", line 1410, in forward out = self.diffusion_model(x, t, context=cc) File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "c:\stable-diffusion\ldm\modules\diffusionmodules\openaimodel.py", line 732, in forward h = module(h, emb, context) File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "c:\stable-diffusion\ldm\modules\diffusionmodules\openaimodel.py", line 85, in forward x = layer(x, context) File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "c:\stable-diffusion\ldm\modules\attention.py", line 258, in forward x = block(x, context=context) File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "c:\stable-diffusion\ldm\modules\attention.py", line 209, in forward return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) File "c:\stable-diffusion\ldm\modules\diffusionmodules\util.py", line 114, in checkpoint return CheckpointFunction.apply(func, len(inputs), *args) File "c:\stable-diffusion\ldm\modules\diffusionmodules\util.py", line 127, in forward output_tensors = ctx.run_function(*ctx.input_tensors) File "c:\stable-diffusion\ldm\modules\attention.py", line 212, in _forward x = self.attn1(self.norm1(x)) + x File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\nn\modules\module.py", line 1110, in _call_impl return forward_call(*input, **kwargs) File "c:\stable-diffusion\ldm\modules\attention.py", line 180, in forward sim = einsum('b i d, b j d -> b i j', q, k) * self.scale File "C:\Users\3vilpcdiva\.conda\envs\ldm\lib\site-packages\torch\functional.py", line 330, in einsum return _VF.einsum(equation, operands) # type: ignore[attr-defined] RuntimeError: CUDA out of memory. Tried to allocate 889.89 GiB (GPU 0; 23.99 GiB total capacity; 5.66 GiB already allocated; 15.42 GiB free; 5.98 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF