thomaslee101
commented
3 months ago as an interim solution, i have added this line just before starting the server. it seems to work for upscaling using LDSR but i'm not sure if it affects "txt to img" and "img to img" functions: os.environ["COMMANDLINE_ARGS"] = '--precision full --no-half'
hi,
i encountered this error: "RuntimeError: mat1 and mat2 must have the same dtype, but got Half and Float" when trying to upscale using LDSR. I am using the latest google collab to run i.e. rename my previous "SD" folder as "SD_older". this error persists even though i use different models SD1.5 and SDXL. below is log of the error. Hope someone can help. thanks.
Startup time: 123.7s (launcher: 1.2s, import torch: 29.8s, import gradio: 2.4s, setup paths: 24.9s, import ldm: 0.7s, initialize shared: 4.5s, other imports: 31.9s, setup codeformer: 0.5s, setup gfpgan: 0.3s, list SD models: 1.6s, load scripts: 17.8s, load upscalers: 1.3s, reload hypernetworks: 0.1s, initialize extra networks: 1.7s, create ui: 3.2s, gradio launch: 0.5s, add APIs: 1.3s). 6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa Loading weights [6ce0161689] from /content/gdrive/MyDrive/sd/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors Creating model from config: /content/gdrive/MyDrive/sd/stable-diffusion-webui/configs/v1-inference.yaml Applying attention optimization: xformers... done. Model loaded in 36.7s (calculate hash: 21.9s, load weights from disk: 1.7s, create model: 4.7s, apply weights to model: 6.5s, hijack: 0.3s, load textual inversion embeddings: 0.6s, calculate empty prompt: 0.8s). Loading model from /content/gdrive/MyDrive/sd/stable-diffusion-webui/models/LDSR/model.ckpt LatentDiffusionV1: Running in eps-prediction mode Keeping EMAs of 308. Applying attention optimization: xformers... done. Downsampling from [494, 1000] to [618, 1250] Plotting: Switched to EMA weights Sampling with eta = 1.0; steps: 100 Data shape for DDIM sampling is (1, 3, 1280, 640), eta 1.0 Running DDIM Sampling with 100 timesteps DDIM Sampler 0% 0/100 [00:00<?, ?it/s] Plotting: Restored training weights Error completing request Arguments: ('task(sh1w0zc1r6yize2)', 0.0, <PIL.Image.Image image mode=RGBA size=494x1000 at 0x7A864DFF7580>, None, '', '', True, True, 0.0, 5, 0.0, 512, 512, True, 'LDSR', 'None', 0, False, 1, False, 1, 0, False, 0.5, 0.2, False, 0.9, 0.15, 0.5, False, False, 384, 768, 4096, 409600, 'Maximize area', 0.1, False, ['Horizontal'], False, ['Deepbooru']) {} Traceback (most recent call last): File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 74, in f res = list(func(*args, kwargs)) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 53, in f res = func(*args, *kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/call_queue.py", line 37, in f res = func(args, kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/postprocessing.py", line 133, in run_postprocessing_webui return run_postprocessing(args, kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/postprocessing.py", line 73, in run_postprocessing scripts.scripts_postproc.run(initial_pp, args) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/scripts_postprocessing.py", line 198, in run script.process(single_image, process_args) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/scripts/postprocessing_upscale.py", line 152, in process upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, max_side_length, upscale_to_width, upscale_to_height, upscale_crop) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/scripts/postprocessing_upscale.py", line 107, in upscale image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/upscaler.py", line 68, in upscale img = self.do_upscale(img, selected_model) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/scripts/ldsr_model.py", line 58, in do_upscale return ldsr.super_resolution(img, ddim_steps, self.scale) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py", line 137, in super_resolution logs = self.run(model["model"], im_padded, diffusion_steps, eta) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py", line 96, in run logs = make_convolutional_sample(example, model, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(args, kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py", line 228, in make_convolutional_sample sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, *kwargs) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/ldsr_model_arch.py", line 184, in convsample_ddim samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(args, kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/ldm/models/diffusion/ddim.py", line 103, in sample samples, intermediates = self.ddim_sampling(conditioning, size, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/ldm/models/diffusion/ddim.py", line 163, in ddim_sampling outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps, File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, kwargs) File "/content/gdrive/MyDrive/sd/stablediffusion/ldm/models/diffusion/ddim.py", line 188, in p_sample_ddim model_output = self.model.apply_model(x, t, c) File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py", line 964, in apply_model output_list = [self.model(z_list[i], t, cond_list[i]) for i in range(z.shape[-1])] File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py", line 964, in
output_list = [self.model(z_list[i], t, *cond_list[i]) for i in range(z.shape[-1])]
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(args, kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py", line 1400, in forward
out = self.diffusion_model(xc, t)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, *kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(args, kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, kwargs)
File "/content/gdrive/MyDrive/sd/stablediffusion/ldm/modules/diffusionmodules/openaimodel.py", line 768, in forward
emb = self.time_embed(t_emb)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, *kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(args, kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, *kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(args, **kwargs)
File "/content/gdrive/MyDrive/sd/stable-diffusion-webui/extensions-builtin/Lora/networks.py", line 584, in network_Linear_forward
return originals.Linear_forward(self, input)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 must have the same dtype, but got Half and Float