Is kohya-hiresfix incompatible with forge?

[Terroosh]

https://github.com/wcde/sd-webui-kohya-hiresfix

If this extension is enabled in the image settings panel nothing will generate with the error TypeError: 'NoneType' object is not iterable
Is this extension not compatible with forge?

Traceback (most recent call last):
  File "K:\SD Forge\webui\modules_forge\main_thread.py", line 37, in loop
    task.work()
  File "K:\SD Forge\webui\modules_forge\main_thread.py", line 26, in work
    self.result = self.func(*self.args, **self.kwargs)
  File "K:\SD Forge\webui\modules\txt2img.py", line 111, in txt2img_function
    processed = processing.process_images(p)
  File "K:\SD Forge\webui\modules\processing.py", line 752, in process_images
    res = process_images_inner(p)
  File "K:\SD Forge\webui\modules\processing.py", line 922, 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 "K:\SD Forge\webui\modules\processing.py", line 1275, in sample
    samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
  File "K:\SD Forge\webui\modules\sd_samplers_kdiffusion.py", line 251, 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 "K:\SD Forge\webui\modules\sd_samplers_common.py", line 263, in launch_sampling
    return func()
  File "K:\SD Forge\webui\modules\sd_samplers_kdiffusion.py", line 251, 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 "K:\SD Forge\system\python\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "K:\SD Forge\webui\repositories\k-diffusion\k_diffusion\sampling.py", line 594, in sample_dpmpp_2m
    denoised = model(x, sigmas[i] * s_in, **extra_args)
  File "K:\SD Forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "K:\SD Forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "K:\SD Forge\webui\modules\sd_samplers_cfg_denoiser.py", line 182, in forward
    denoised = forge_sampler.forge_sample(self, denoiser_params=denoiser_params,
  File "K:\SD Forge\webui\modules_forge\forge_sampler.py", line 88, in forge_sample
    denoised = sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options, seed)
  File "K:\SD Forge\webui\ldm_patched\modules\samplers.py", line 289, in sampling_function
    cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
  File "K:\SD Forge\webui\ldm_patched\modules\samplers.py", line 258, in calc_cond_uncond_batch
    output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
  File "K:\SD Forge\webui\ldm_patched\modules\model_base.py", line 90, in apply_model
    model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
  File "K:\SD Forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "K:\SD Forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "K:\SD Forge\webui\ldm_patched\ldm\modules\diffusionmodules\openaimodel.py", line 867, in forward
    h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
  File "K:\SD Forge\webui\ldm_patched\ldm\modules\diffusionmodules\openaimodel.py", line 42, in forward_timestep_embed
    for layer_index, layer in enumerate(ts):
TypeError: 'Scaler' object is not iterable
'Scaler' object is not iterable
*** Error completing request
*** Arguments: ('task(g4suagzxr96r7vp)', <gradio.routes.Request object at 0x00000297A945DAE0>, 'bowling ball,', '', [], 20, 'DPM++ 2M Karras', 1, 1, 7, 768, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', '', '', [], 0, False, '', 0.8, -1, False, -1, 0, 0, 0, 0, False, 1, False, True, True, 3, 4, 0.3, 0.3, 'nearest-exact', 0.25, 2, True, True, False, 0.75, 1, ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=[], batch_mask_gallery=[], generated_image=None, mask_image=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=[], batch_mask_gallery=[], generated_image=None, mask_image=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=[], batch_mask_gallery=[], generated_image=None, mask_image=None, hr_option='Both', enabled=False, module='None', model='None', weight=1, image=None, resize_mode='Crop and Resize', processor_res=-1, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), False, 7, 1, 'Constant', 0, 'Constant', 0, 1, 'enable', 'MEAN', 'AD', 1, False, 1.01, 1.02, 0.99, 0.95, False, 0.5, 2, False, 256, 2, 0, False, False, 0, 'anisotropic', 0, 'reinhard', 100, 0, 'subtract', 0, 0, 'gaussian', 'add', 0, 100, 127, 0, 'hard_clamp', 5, 0, 'None', 'None', False, 'MultiDiffusion', 768, 768, 64, 4, False, False, False, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
    Traceback (most recent call last):
      File "K:\SD Forge\webui\modules\call_queue.py", line 57, in f
        res = list(func(*args, **kwargs))
    TypeError: 'NoneType' object is not iterable

---

[wgku]

I have the same problem
`Traceback (most recent call last):
File "/content/SDWCT/modules_forge/main_thread.py", line 37, in loop
task.work()
File "/content/SDWCT/modules_forge/main_thread.py", line 26, in work
self.result = self.func(*self.args, **self.kwargs)
File "/content/SDWCT/modules/txt2img.py", line 114, in txt2img_function
processed = processing.process_images(p)
File "/content/SDWCT/modules/processing.py", line 823, in process_images
res = process_images_inner(p)
File "/content/SDWCT/modules/processing.py", line 981, 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 "/content/SDWCT/modules/processing.py", line 1356, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "/content/SDWCT/modules/sd_samplers_kdiffusion.py", line 295, 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 "/content/SDWCT/modules/sd_samplers_common.py", line 277, in launch_sampling
return func()
File "/content/SDWCT/modules/sd_samplers_kdiffusion.py", line 295, in
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 "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/content/SDWCT/repositories/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self.call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in call_impl
return forward_call(*args, **kwargs)
File "/content/SDWCT/modules/sd_samplers_cfg_denoiser.py", line 228, in forward
denoised = sampling_function(model, x, sigma, uncond_patched, cond_patched, cond_scale, model_options, seed)
File "/content/SDWCT/ldm_patched/modules/samplers.py", line 299, in sampling_function
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options)
File "/content/SDWCT/ldm_patched/modules/samplers.py", line 262, in calc_cond_uncond_batch
output = model.apply_model(input_x, timestep, **c).chunk(batch_chunks)
File "/content/SDWCT/ldm_patched/modules/model_base.py", line 90, in apply_model
model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/content/SDWCT/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py", line 886, in forward
h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
File "/content/SDWCT/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py", line 42, in forward_timestep_embed
for layer_index, layer in enumerate(ts):
TypeError: 'Scaler' object is not iterable
'Scaler' object is not iterable
*** Error completing request
*** Arguments: ('task(s7z28mij5peuude)', <gradio.routes.Request object at 0x7ac73d7799f0>, '(masterpiece,best quality,very aesthetic,photorealistic),1girl,perfect anatomy,(full body:1.5),moon night light lake glass,depth of field,detailed background,fountain,street,chinese style architecture,forest,(solo),ponytail,crossed bangs,looking at viewer,delicate face,smile,blush,delicate features,Bracelet,earrings,bracelets,necklaces,hair clips,pose,(full nude:1.2),(large breasts),(perfect nipples),(perfect pussy),swollen smooth labia,wind_lift,', 'bokeh,close-up,(fingers:1),text,watermark,[negative_hand-neg:0.2],black and white,[low quality, lowres, overexposure, sketch, blurry, bad anatomy, pixelated, distorted, grainy, over-sharpened, artifacts, washed-out, jagged:0.2],[negative_hand-neg:0.2],', [], 1, 1, 5, 1024, 1024, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 30, 'DPM++ 2M', 'Karras', False, '', 0.8, 1588993364, False, -1, 0, 0, 0, False, False, False, 'SDXL', '', 'Equal Weights', 832, 1216, False, '', 0, False, '', 0.5, True, False, '', 'Lerp', False, True, True, 4, 3, 0.3, 0.15, 'bicubic', 0.5, 2, True, False, ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=[], batch_mask_gallery=[], multi_inputs_gallery=[], generated_image=None, mask_image=None, hr_option=<HiResFixOption.BOTH: 'Both'>, enabled=False, module='None', model='None', weight=1, image=None, resize_mode=<ResizeMode.INNER_FIT: 'Crop and Resize'>, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode=<ControlMode.BALANCED: 'Balanced'>, advanced_weighting=None, save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=[], batch_mask_gallery=[], multi_inputs_gallery=[], generated_image=None, mask_image=None, hr_option=<HiResFixOption.BOTH: 'Both'>, enabled=False, module='None', model='None', weight=1, image=None, resize_mode=<ResizeMode.INNER_FIT: 'Crop and Resize'>, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode=<ControlMode.BALANCED: 'Balanced'>, advanced_weighting=None, save_detected_map=True), ControlNetUnit(input_mode=<InputMode.SIMPLE: 'simple'>, use_preview_as_input=False, batch_image_dir='', batch_mask_dir='', batch_input_gallery=[], batch_mask_gallery=[], multi_inputs_gallery=[], generated_image=None, mask_image=None, hr_option=<HiResFixOption.BOTH: 'Both'>, enabled=False, module='None', model='None', weight=1, image=None, resize_mode=<ResizeMode.INNER_FIT: 'Crop and Resize'>, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode=<ControlMode.BALANCED: 'Balanced'>, advanced_weighting=None, save_detected_map=True), False, False, 7, 1, 'Constant', 0, 'Constant', 0, 1, 'enable', 'MEAN', 'AD', 1, False, 1.01, 1.02, 0.99, 0.95, False, 0.5, 2, False, 256, 2, 0, False, False, 3, 2, 0, 0.35, True, 'bicubic', 'bicubic', False, 0.5, 0.18, 15, 1, False, 0.7, False, 'Discrete', 'v_prediction', True, 'v_prediction', 120, 0.002, False, 'MultiDiffusion', 768, 768, 64, 4, False, False, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, 0, False, False, False, False, False, False, 0, False) {}
Traceback (most recent call last):
File "/content/SDWCT/modules/call_queue.py", line 74, in f
res = list(func(*args, **kwargs))
TypeError: 'NoneType' object is not iterable

---

`