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v8hid / infinite-zoom-automatic1111-webui

infinite zoom effect extension for AUTOMATIC1111's webui - stable diffusion
MIT License
654 stars 62 forks source link

error, not working #74

Closed gndqb closed 1 year ago

gndqb commented 1 year ago

Describe the bug Returns error, not working. It used to work before.

To Reproduce Steps to reproduce the behavior:

  1. Run Automatic1111
  2. Infinite zoom tab, all initial settings. Click on 'Generate Video'...
  3. returns error

Expected behavior Should generate video.

Screenshots of error

Batch 1/1
  0%|                                                   | 0/50 [00:01<?, ?it/s]
Error completing request
Arguments: ([[0, 'Huge spectacular Waterfall in a dense tropical forest,epic perspective,(vegetation overgrowth:1.3)(intricate, ornamentation:1.1),(baroque:1.1), fantasy, (realistic:1) digital painting , (magical,mystical:1.2) , (wide angle shot:1.4), (landscape composed:1.2)(medieval:1.1), divine,cinematic,(tropical forest:1.4),(river:1.3)mythology,india, volumetric lighting, Hindu ,epic,  Alex Horley Wenjun Lin greg rutkowski Ruan Jia (Wayne Barlowe:1.2) <lora:epiNoiseoffset_v2:0.6> ']], 'frames, borderline, text, character, duplicate, error, out of frame, watermark, low quality, ugly, deformed, blur  bad-artist', 8, 7, 50, None, None, 30, 0, 0, 0, 1, 0, 2, False, 0, 1, 512, 512, 1, 'Euler a', False, 'None', 1) {}
Traceback (most recent call last):
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\call_queue.py", line 56, in f
    res = list(func(*args, **kwargs))
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\call_queue.py", line 37, in f
    res = func(*args, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\extensions\infinite-zoom-automatic1111-webui\scripts\infinite-zoom.py", line 242, in create_zoom
    result = create_zoom_single(
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\extensions\infinite-zoom-automatic1111-webui\scripts\infinite-zoom.py", line 330, in create_zoom_single
    processed = renderTxt2Img(
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\extensions\infinite-zoom-automatic1111-webui\scripts\infinite-zoom.py", line 132, in renderTxt2Img
    processed = process_images(p)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\processing.py", line 503, in process_images
    res = process_images_inner(p)
  File "D:\Graphics\stable-diffusion-webui\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 "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\processing.py", line 653, in process_images_inner
    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\processing.py", line 869, in sample
    samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 358, in sample
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 234, in launch_sampling
    return func()
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 358, in <lambda>
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\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 "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 126, in forward
    x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 167, in forward
    return self.get_v(input * c_in, self.sigma_to_t(sigma), **kwargs) * c_out + input * c_skip
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 177, in get_v
    return self.inner_model.apply_model(x, t, cond)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_hijack_utils.py", line 17, in <lambda>
    setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_hijack_utils.py", line 28, in __call__
    return self.__orig_func(*args, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 858, in apply_model
    x_recon = self.model(x_noisy, t, **cond)
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 1335, in forward
    out = self.diffusion_model(x, t, context=cc)
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 797, in forward
    h = module(h, emb, context)
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 84, in forward
    x = layer(x, context)
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 334, in forward
    x = block(x, context=context[i])
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 269, in forward
    return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\util.py", line 121, in checkpoint
    return CheckpointFunction.apply(func, len(inputs), *args)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\util.py", line 136, in forward
    output_tensors = ctx.run_function(*ctx.input_tensors)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\attention.py", line 273, in _forward
    x = self.attn2(self.norm2(x), context=context) + x
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\modules\sd_hijack_optimizations.py", line 332, in xformers_attention_forward
    k_in = self.to_k(context_k)
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\extensions-builtin\Lora\lora.py", line 305, in lora_Linear_forward
    lora_apply_weights(self)
  File "D:\Graphics\stable-diffusion-webui\stable-diffusion-webui\extensions-builtin\Lora\lora.py", line 273, in lora_apply_weights
    self.weight += lora_calc_updown(lora, module, self.weight)
RuntimeError: The size of tensor a (1024) must match the size of tensor b (768) at non-singleton dimension 1

Traceback (most recent call last):
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\gradio\blocks.py", line 987, in postprocess_data
    if predictions[i] is components._Keywords.FINISHED_ITERATING:
IndexError: tuple index out of range

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\gradio\routes.py", line 394, in run_predict
    output = await app.get_blocks().process_api(
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\gradio\blocks.py", line 1078, in process_api
    data = self.postprocess_data(fn_index, result["prediction"], state)
  File "d:\Graphics\stable-diffusion-webui\stable-diffusion-webui\venv\lib\site-packages\gradio\blocks.py", line 991, in postprocess_data
    raise ValueError(
ValueError: Number of output components does not match number of values returned from from function f

Desktop (please complete the following information):

Additional context I was able to use it before, seems something broke after updates.

GeorgLegato commented 1 year ago

the Lora prompt fails , most probably not compatible with your model. try again remove the lora part.

then check your infinite zoom settings in Settings Tab define two models, for txt2img and one for inpainting, we switch between both. if you use loras they have to be compatible, 1.5/2.1 stuff