SDXL Img2Img problems - any ideas?

[CliveBryant]

Problem fixed! (can't delete it, and might help others)

Original problem:
Using SDXL in A1111.
no problems in txt2img, but when I use img2img, I get:
"NansException: A tensor with all NaNs was produced in Unet. This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the "Upcast cross attention layer to float32" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this. Use --disable-nan-check commandline argument to disable this check."

I get this for both xl_base_1.0.safetensors and xl_refiner_1.0.safetensors

img2img works fine for all other checkpoints.
Tried SD VAE on both automatic and sdxl_vae-safetensors

Running on Windows system with Nvidia 12GB GeForce RTX 3060

--disable-nan-check results in a black image
--no-half causes an error in the startup batch file
changing the setting for Upcase cross attention... makes no difference
changing the resolution of the input and output images makes no difference

adding --medvram and --no-half startup arguments resolved the issue.
Thought I'd tried that combination previously - obviously not!!!!! (doh!)

[freecoderwaifu]

In the latest commits you can also enable "Automaticlly revert VAE to 32-bit floats" in Settings > Stable Diffusion without --no-half and --no-half-vae

Actually saw it in action for the first time today, image generation stopped for a bit but then continued and finished normally without black squares.

[CliveBryant]

Thanks for pointing this out. Unfortunately, it was already checked for me, so did not provide an alternative solution (it may need this checked also, of course!).

[Akis-M]

Still not working for me after OP suggestions. Getting this error now:

*** Error completing request
*** Arguments: ('task(ay4nillhr964tek)', 'test', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 0, '', '', [], <gradio.routes.Request object at 0x000002A51FA4D090>, 0, True, False, 1, False, False, False, 1.1, 1.5, 100, 0.7, False, False, True, False, False, 0, 'Gustavosta/MagicPrompt-Stable-Diffusion', '', <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002A51FA4D8D0>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002A51FA4D330>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002A51F669AE0>, 2, 150, True, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, None, None, False, None, None, False, None, None, False, 50) {}
Traceback (most recent call last):
File "C:\stable-diffusion-webui\modules\call_queue.py", line 58, in f
res = list(func(*args, **kwargs))
File "C:\stable-diffusion-webui\modules\call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "C:\stable-diffusion-webui\modules\txt2img.py", line 62, in txt2img
processed = processing.process_images(p)
File "C:\stable-diffusion-webui\modules\processing.py", line 677, in process_images
res = process_images_inner(p)
File "C:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "C:\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 "C:\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 "C:\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 "C:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 303, in launch_sampling
return func()
File "C:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 464, in
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "C:\stable-diffusion-webui\venv\pyenv.cfg\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "C:\stable-diffusion-webui\venv\pyenv.cfg\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "C:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 215, in forward
devices.test_for_nans(x_out, "unet")
File "C:\stable-diffusion-webui\modules\devices.py", line 155, in test_for_nans
raise NansException(message)
modules.devices.NansException: A tensor with all NaNs was produced in Unet. Use --disable-nan-check commandline argument to disable this check.

---

0%| | 0/20 [00:01<?, ?it/s]
*** Error completing request
*** Arguments: ('task(cx4jvun9ttl7x3n)', 'test', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 0, '', '', [], <gradio.routes.Request object at 0x000002A51F796A40>, 0, True, False, 1, False, False, False, 1.1, 1.5, 100, 0.7, False, False, True, False, False, 0, 'Gustavosta/MagicPrompt-Stable-Diffusion', '', <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002A51F64FCD0>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002A51F794910>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002A51F7976A0>, 2, 150, True, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, None, None, False, None, None, False, None, None, False, 50) {}
Traceback (most recent call last):
File "C:\stable-diffusion-webui\modules\call_queue.py", line 58, in f
res = list(func(*args, **kwargs))
File "C:\stable-diffusion-webui\modules\call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "C:\stable-diffusion-webui\modules\txt2img.py", line 62, in txt2img
processed = processing.process_images(p)
File "C:\stable-diffusion-webui\modules\processing.py", line 677, in process_images
res = process_images_inner(p)
File "C:\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "C:\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 "C:\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 "C:\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 "C:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 303, in launch_sampling
return func()
File "C:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 464, in
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "C:\stable-diffusion-webui\venv\pyenv.cfg\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "C:\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "C:\stable-diffusion-webui\venv\pyenv.cfg\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "C:\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 215, in forward
devices.test_for_nans(x_out, "unet")
File "C:\stable-diffusion-webui\modules\devices.py", line 155, in test_for_nans
raise NansException(message)
modules.devices.NansException: A tensor with all NaNs was produced in Unet. Use --disable-nan-check commandline argument to disable this check.

Any ideas? Also I don't have SDXL vae, i'm not sure if it matters. In fact simply selecting SDXL VAE results in an error of its own:
Loading VAE weights specified in settings: C:\stable-diffusion-webui\models\VAE\sdXL_v10VAEFix.safetensors
changing setting sd_vae to sdXL_v10VAEFix.safetensors: RuntimeError
Traceback (most recent call last):
File "C:\stable-diffusion-webui\modules\shared.py", line 633, in set
self.data_labels[key].onchange()
File "C:\stable-diffusion-webui\modules\call_queue.py", line 14, in f
res = func(*args, **kwargs)
File "C:\stable-diffusion-webui\webui.py", line 239, in
shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
File "C:\stable-diffusion-webui\modules\sd_vae.py", line 204, in reload_vae_weights
load_vae(sd_model, vae_file, vae_source)
File "C:\stable-diffusion-webui\modules\sd_vae.py", line 143, in load_vae
_load_vae_dict(model, vae_dict_1)
File "C:\stable-diffusion-webui\modules\sd_vae.py", line 168, in _load_vae_dict
model.first_stage_model.load_state_dict(vae_dict_1)
File "C:\stable-diffusion-webui\venv\pyenv.cfg\lib\site-packages\torch\nn\modules\module.py", line 2041, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for AutoencoderKL:
Missing key(s) in state_dict: "encoder.conv_in.weight", "encoder.conv_in.bias", "encoder.down.0.block.0.norm1.weight", "encoder.down.0.block.0.norm1.bias", "encoder.down.0.block.0.conv1.weight", "encoder.down.0.block.0.conv1.bias", "encoder.down.0.block.0.norm2.weight", "encoder.down.0.block.0.norm2.bias", "encoder.down.0.block.0.conv2.weight", "encoder.down.0.block.0.conv2.bias", "encoder.down.0.block.1.norm1.weight", "encoder.down.0.block.1.norm1.bias", "encoder.down.0.block.1.conv1.weight", "encoder.down.0.block.1.conv1.bias", "encoder.down.0.block.1.norm2.weight", "encoder.down.0.block.1.norm2.bias", "encoder.down.0.block.1.conv2.weight", "encoder.down.0.block.1.conv2.bias", "encoder.down.0.downsample.conv.weight", "encoder.down.0.downsample.conv.bias", "encoder.down.1.block.0.norm1.weight", "encoder.down.1.block.0.norm1.bias", "encoder.down.1.block.0.conv1.weight", "encoder.down.1.block.0.conv1.bias", "encoder.down.1.block.0.norm2.weight", "encoder.down.1.block.0.norm2.bias", "encoder.down.1.block.0.conv2.weight", "encoder.down.1.block.0.conv2.bias", "encoder.down.1.block.0.nin_shortcut.weight", "encoder.down.1.block.0.nin_shortcut.bias", "encoder.down.1.block.1.norm1.weight", "encoder.down.1.block.1.norm1.bias", "encoder.down.1.block.1.conv1.weight", "encoder.down.1.block.1.conv1.bias", "encoder.down.1.block.1.norm2.weight", "encoder.down.1.block.1.norm2.bias", "encoder.down.1.block.1.conv2.weight", "encoder.down.1.block.1.conv2.bias", "encoder.down.1.downsample.conv.weight", "encoder.down.1.downsample.conv.bias", "encoder.down.2.block.0.norm1.weight", "encoder.down.2.block.0.norm1.bias", "encoder.down.2.block.0.conv1.weight", "encoder.down.2.block.0.conv1.bias", "encoder.down.2.block.0.norm2.weight", "encoder.down.2.block.0.norm2.bias", "encoder.down.2.block.0.conv2.weight", "encoder.down.2.block.0.conv2.bias", "encoder.down.2.block.0.nin_shortcut.weight", "encoder.down.2.block.0.nin_shortcut.bias", "encoder.down.2.block.1.norm1.weight", "encoder.down.2.block.1.norm1.bias", "encoder.down.2.block.1.conv1.weight", "encoder.down.2.block.1.conv1.bias", "encoder.down.2.block.1.norm2.weight", "encoder.down.2.block.1.norm2.bias", "encoder.down.2.block.1.conv2.weight", "encoder.down.2.block.1.conv2.bias", "encoder.down.2.downsample.conv.weight", "encoder.down.2.downsample.conv.bias", "encoder.down.3.block.0.norm1.weight", "encoder.down.3.block.0.norm1.bias", "encoder.down.3.block.0.conv1.weight", "encoder.down.3.block.0.conv1.bias", "encoder.down.3.block.0.norm2.weight", "encoder.down.3.block.0.norm2.bias", "encoder.down.3.block.0.conv2.weight", "encoder.down.3.block.0.conv2.bias", "encoder.down.3.block.1.norm1.weight", "encoder.down.3.block.1.norm1.bias", "encoder.down.3.block.1.conv1.weight", "encoder.down.3.block.1.conv1.bias", "encoder.down.3.block.1.norm2.weight", "encoder.down.3.block.1.norm2.bias", "encoder.down.3.block.1.conv2.weight", "encoder.down.3.block.1.conv2.bias", "encoder.mid.block_1.norm1.weight", "encoder.mid.block_1.norm1.bias", "encoder.mid.block_1.conv1.weight", "encoder.mid.block_1.conv1.bias", "encoder.mid.block_1.norm2.weight", "encoder.mid.block_1.norm2.bias", "encoder.mid.block_1.conv2.weight", "encoder.mid.block_1.conv2.bias", "encoder.mid.attn_1.norm.weight", "encoder.mid.attn_1.norm.bias", "encoder.mid.attn_1.q.weight", "encoder.mid.attn_1.q.bias", "encoder.mid.attn_1.k.weight", "encoder.mid.attn_1.k.bias", "encoder.mid.attn_1.v.weight", "encoder.mid.attn_1.v.bias", "encoder.mid.attn_1.proj_out.weight", "encoder.mid.attn_1.proj_out.bias", "encoder.mid.block_2.norm1.weight", "encoder.mid.block_2.norm1.bias", "encoder.mid.block_2.conv1.weight", "encoder.mid.block_2.conv1.bias", "encoder.mid.block_2.norm2.weight", "encoder.mid.block_2.norm2.bias", "encoder.mid.block_2.conv2.weight", "encoder.mid.block_2.conv2.bias", "encoder.norm_out.weight", "encoder.norm_out.bias", "encoder.conv_out.weight", "encoder.conv_out.bias", "decoder.conv_in.weight", "decoder.conv_in.bias", "decoder.mid.block_1.norm1.weight", "decoder.mid.block_1.norm1.bias", "decoder.mid.block_1.conv1.weight", "decoder.mid.block_1.conv1.bias", "decoder.mid.block_1.norm2.weight", "decoder.mid.block_1.norm2.bias", "decoder.mid.block_1.conv2.weight", "decoder.mid.block_1.conv2.bias", "decoder.mid.attn_1.norm.weight", "decoder.mid.attn_1.norm.bias", "decoder.mid.attn_1.q.weight", "decoder.mid.attn_1.q.bias", "decoder.mid.attn_1.k.weight", "decoder.mid.attn_1.k.bias", "decoder.mid.attn_1.v.weight", "decoder.mid.attn_1.v.bias", "decoder.mid.attn_1.proj_out.weight", "decoder.mid.attn_1.proj_out.bias", "decoder.mid.block_2.norm1.weight", "decoder.mid.block_2.norm1.bias", "decoder.mid.block_2.conv1.weight", "decoder.mid.block_2.conv1.bias", "decoder.mid.block_2.norm2.weight", "decoder.mid.block_2.norm2.bias", "decoder.mid.block_2.conv2.weight", "decoder.mid.block_2.conv2.bias", "decoder.up.0.block.0.norm1.weight", "decoder.up.0.block.0.norm1.bias", "decoder.up.0.block.0.conv1.weight", "decoder.up.0.block.0.conv1.bias", "decoder.up.0.block.0.norm2.weight", "decoder.up.0.block.0.norm2.bias", "decoder.up.0.block.0.conv2.weight", "decoder.up.0.block.0.conv2.bias", "decoder.up.0.block.0.nin_shortcut.weight", "decoder.up.0.block.0.nin_shortcut.bias", "decoder.up.0.block.1.norm1.weight", "decoder.up.0.block.1.norm1.bias", "decoder.up.0.block.1.conv1.weight", "decoder.up.0.block.1.conv1.bias", "decoder.up.0.block.1.norm2.weight", "decoder.up.0.block.1.norm2.bias", "decoder.up.0.block.1.conv2.weight", "decoder.up.0.block.1.conv2.bias", "decoder.up.0.block.2.norm1.weight", "decoder.up.0.block.2.norm1.bias", "decoder.up.0.block.2.conv1.weight", "decoder.up.0.block.2.conv1.bias", "decoder.up.0.block.2.norm2.weight", "decoder.up.0.block.2.norm2.bias", "decoder.up.0.block.2.conv2.weight", "decoder.up.0.block.2.conv2.bias", "decoder.up.1.block.0.norm1.weight", "decoder.up.1.block.0.norm1.bias", "decoder.up.1.block.0.conv1.weight", "decoder.up.1.block.0.conv1.bias", "decoder.up.1.block.0.norm2.weight", "decoder.up.1.block.0.norm2.bias", "decoder.up.1.block.0.conv2.weight", "decoder.up.1.block.0.conv2.bias", "decoder.up.1.block.0.nin_shortcut.weight", "decoder.up.1.block.0.nin_shortcut.bias", "decoder.up.1.block.1.norm1.weight", "decoder.up.1.block.1.norm1.bias", "decoder.up.1.block.1.conv1.weight", "decoder.up.1.block.1.conv1.bias", "decoder.up.1.block.1.norm2.weight", "decoder.up.1.block.1.norm2.bias", "decoder.up.1.block.1.conv2.weight", "decoder.up.1.block.1.conv2.bias", "decoder.up.1.block.2.norm1.weight", "decoder.up.1.block.2.norm1.bias", "decoder.up.1.block.2.conv1.weight", "decoder.up.1.block.2.conv1.bias", "decoder.up.1.block.2.norm2.weight", "decoder.up.1.block.2.norm2.bias", "decoder.up.1.block.2.conv2.weight", "decoder.up.1.block.2.conv2.bias", "decoder.up.1.upsample.conv.weight", "decoder.up.1.upsample.conv.bias", "decoder.up.2.block.0.norm1.weight", "decoder.up.2.block.0.norm1.bias", "decoder.up.2.block.0.conv1.weight", "decoder.up.2.block.0.conv1.bias", "decoder.up.2.block.0.norm2.weight", "decoder.up.2.block.0.norm2.bias", "decoder.up.2.block.0.conv2.weight", "decoder.up.2.block.0.conv2.bias", "decoder.up.2.block.1.norm1.weight", "decoder.up.2.block.1.norm1.bias", "decoder.up.2.block.1.conv1.weight", "decoder.up.2.block.1.conv1.bias", "decoder.up.2.block.1.norm2.weight", "decoder.up.2.block.1.norm2.bias", "decoder.up.2.block.1.conv2.weight", "decoder.up.2.block.1.conv2.bias", "decoder.up.2.block.2.norm1.weight", "decoder.up.2.block.2.norm1.bias", "decoder.up.2.block.2.conv1.weight", "decoder.up.2.block.2.conv1.bias", "decoder.up.2.block.2.norm2.weight", "decoder.up.2.block.2.norm2.bias", "decoder.up.2.block.2.conv2.weight", "decoder.up.2.block.2.conv2.bias", "decoder.up.2.upsample.conv.weight", "decoder.up.2.upsample.conv.bias", "decoder.up.3.block.0.norm1.weight", "decoder.up.3.block.0.norm1.bias", "decoder.up.3.block.0.conv1.weight", "decoder.up.3.block.0.conv1.bias", "decoder.up.3.block.0.norm2.weight", "decoder.up.3.block.0.norm2.bias", "decoder.up.3.block.0.conv2.weight", "decoder.up.3.block.0.conv2.bias", "decoder.up.3.block.1.norm1.weight", "decoder.up.3.block.1.norm1.bias", "decoder.up.3.block.1.conv1.weight", "decoder.up.3.block.1.conv1.bias", "decoder.up.3.block.1.norm2.weight", "decoder.up.3.block.1.norm2.bias", "decoder.up.3.block.1.conv2.weight", "decoder.up.3.block.1.conv2.bias", "decoder.up.3.block.2.norm1.weight", "decoder.up.3.block.2.norm1.bias", "decoder.up.3.block.2.conv1.weight", "decoder.up.3.block.2.conv1.bias", "decoder.up.3.block.2.norm2.weight", "decoder.up.3.block.2.norm2.bias", "decoder.up.3.block.2.conv2.weight", "decoder.up.3.block.2.conv2.bias", "decoder.up.3.upsample.conv.weight", "decoder.up.3.upsample.conv.bias", "decoder.norm_out.weight", "decoder.norm_out.bias", "decoder.conv_out.weight", "decoder.conv_out.bias", "quant_conv.weight", "quant_conv.bias", "post_quant_conv.weight", "post_quant_conv.bias".
Unexpected key(s) in state_dict: "conditioner.embedders.0.transformer.text_model.embeddings.position_embedding.weight", "conditioner.embedders.0.transformer.text_model.embeddings.position_ids", "conditioner.embedders.0.transformer.text_model.embeddings.token_embedding.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.layer_norm2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.mlp.fc2.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.k_proj.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.out_proj.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.q_proj.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.0.self_attn.v_proj.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.bias", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm1.weight", "conditioner.embedders.0.transformer.text_model.encoder.layers.1.layer_norm2.bias", 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[crackhopper]

I fix a similar problem by:

1. finding the "File "C:\stable-diffusion-webui\modules\sd_vae.py", line 168, in _load_vae_dict"
2. modify the code from model.first_stage_model.load_state_dict(vae_dict_1) to model.first_stage_model.load_state_dict(vae_dict_1, strict=False)

I refer this modification from : https://stackoverflow.com/questions/54058256/runtimeerror-errors-in-loading-state-dict-for-resnet

it works.

[Hoppsss]

Unfortunately this does not resolve this issue for me. --no-half allows img2img to work but uses more VRAM than I'd like.

[HenrysCat]

Same problem same GPU, tried all suggestions above nothing works.

[Finkmeister]

Hello I'm using (img2img alternative test) option in Img2img and when I click on generate I get this error : TypeError: expected Tensor as element 0 in argument 0, but got dict

Is this related to your issue?

[Hoppsss]

This post is related to a general img2img issue whereas your issue is related to the img2img alternative test script not being updated for SDXL as of yet.

[Finkmeister]

Do you know if they are working on solving this or when it will be available? thanks

[Hoppsss]

I made a separate post about this issue and unfortunately no progress has been made yet.

[Finkmeister]

Thanks man!