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width/height validation

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This commit is contained in:
Vladimir Mandic
2023-12-18 18:51:56 -05:00
parent 3ee3f30046
commit 0482d5a448
2 changed files with 12 additions and 11 deletions
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modules/processing_diffusers.py
+11 -10
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@@ -94,10 +94,11 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
i = (step + 1) % len(p.prompt_embeds)
kwargs["prompt_embeds"] = p.prompt_embeds[i][0:1].repeat(1, kwargs["prompt_embeds"].shape[0], 1).view(
kwargs["prompt_embeds"].shape[0], kwargs["prompt_embeds"].shape[1], -1)
kwargs["negative_prompt_embeds"] = p.negative_embeds[i][0:1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
j = (step + 1) % len(p.negative_embeds)
kwargs["negative_prompt_embeds"] = p.negative_embeds[j][0:1].repeat(1, kwargs["negative_prompt_embeds"].shape[0], 1).view(
kwargs["negative_prompt_embeds"].shape[0], kwargs["negative_prompt_embeds"].shape[1], -1)
except Exception as e:
shared.log.debug(f"Callback: {e}")
shared.log.debug(f"Callback: {e}")
shared.state.current_latent = kwargs['latents']
if shared.cmd_opts.profile and shared.profiler is not None:
shared.profiler.step()
@@ -244,8 +245,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
elif sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INSTRUCT and len(getattr(p, 'init_images' ,[])) > 0:
p.ops.append('instruct')
task_args = {
'width': 8 * math.ceil(p.width / 8),
'height': 8 * math.ceil(p.height / 8),
'width': 8 * math.ceil(p.width / 8) if hasattr(p, 'width') else None,
'height': 8 * math.ceil(p.height / 8) if hasattr(p, 'height') else None,
'image': p.init_images,
'strength': p.denoising_strength,
}
@@ -289,8 +290,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
init_latent = (1 - p.denoising_strength) * init_latent + init_noise
task_args = {
'latents': init_latent.to(model.dtype),
'width': p.width,
'height': p.height,
'width': p.width if hasattr(p, 'width') else None,
'height': p.height if hasattr(p, 'height') else None,
}
return task_args
@@ -408,8 +409,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
def recompile_model(hires=False):
if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
if shared.opts.cuda_compile_backend == "openvino_fx":
compile_height = p.height if not hires else p.hr_upscale_to_y
compile_width = p.width if not hires else p.hr_upscale_to_x
compile_height = p.height if not hires and hasattr(p, 'height') else p.hr_upscale_to_y
compile_width = p.width if not hires and hasattr(p, 'width') else p.hr_upscale_to_x
if (shared.compiled_model_state is None or
(not shared.compiled_model_state.first_pass
and (shared.compiled_model_state.height != compile_height
@@ -509,7 +510,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if hasattr(shared.sd_model, 'unet') and hasattr(shared.sd_model.unet, 'config') and hasattr(shared.sd_model.unet.config, 'in_channels') and shared.sd_model.unet.config.in_channels == 9:
shared.sd_model = sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.INPAINTING) # force pipeline
if len(getattr(p, 'init_images' ,[])) == 0:
p.init_images = [TF.to_pil_image(torch.rand((3, p.height, p.width)))]
p.init_images = [TF.to_pil_image(torch.rand((3, getattr(p, 'height', 512), getattr(p, 'width', 512))))]
base_args = set_pipeline_args(
model=shared.sd_model,
prompts=prompts,
@@ -567,7 +568,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if p.is_hr_pass:
p.init_hr()
prev_job = shared.state.job
if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
if hasattr(p, 'height') and hasattr(p, 'width') and (p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y):
p.ops.append('upscale')
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_highres_fix and hasattr(shared.sd_model, 'vae'):
save_intermediate(latents=output.images, suffix="-before-hires")