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refactor diffusers tasks

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Vladimir Mandic
2023-09-29 14:13:27 -04:00
parent dc31dcbc1c
commit 965e5a95f1
6 changed files with 78 additions and 71 deletions
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modules/processing.py
+7 -16
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@@ -516,10 +516,10 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts, all_seeds, all_su
args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}"
args["Init image hash"] = getattr(p, 'init_img_hash', None)
args["Conditional mask weight"] = getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None
args['Resize mode'] = p.resize_mode
args["Mask blur"] = p.mask_blur if p.mask is not None and p.mask_blur > 0 else None
args["Noise multiplier"] = p.initial_noise_multiplier if p.initial_noise_multiplier != 1.0 else None
args["Denoising strength"] = p.denoising_strength
args['Resize mode'] = getattr(p, 'resize_mode', None)
args["Mask blur"] = p.mask_blur if getattr(p, 'mask', None) is not None and getattr(p, 'mask_blur', 0) > 0 else None
args["Noise multiplier"] = p.initial_noise_multiplier if getattr(p, 'initial_noise_multiplier', 1.0) != 1.0 else None
args["Denoising strength"] = getattr(p, 'denoising_strength', None)
if 'face' in p.ops:
args["Face restoration"] = shared.opts.face_restoration_model
if 'color' in p.ops:
@@ -933,7 +933,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
def init(self, all_prompts, all_seeds, all_subseeds):
if shared.backend == shared.Backend.DIFFUSERS:
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
self.width = self.width or 512
self.height = self.height or 512
@@ -990,9 +990,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.restore_faces = orig2
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], shared.opts.samples_format, info=info, suffix="-before-hires")
if shared.backend == shared.Backend.DIFFUSERS:
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.TEXT_2_IMAGE)
latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
if latent_scale_mode is not None:
self.hr_force = False # no need to force anything
@@ -1097,10 +1094,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
def init(self, all_prompts, all_seeds, all_subseeds):
if shared.backend == shared.Backend.DIFFUSERS and self.image_mask is not None:
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
self.sd_model.dtype = self.sd_model.unet.dtype
elif shared.backend == shared.Backend.DIFFUSERS and self.image_mask is None:
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
shared.sd_model = modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
if self.sampler_name == "PLMS":
self.sampler_name = 'UniPC'
@@ -1213,12 +1210,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
if shared.backend == shared.Backend.DIFFUSERS and self.image_mask is not None:
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.INPAINTING)
self.sd_model.dtype = self.sd_model.unet.dtype
elif shared.backend == shared.Backend.DIFFUSERS and self.image_mask is None:
modules.sd_models.set_diffuser_pipe(self.sd_model, modules.sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
x = create_random_tensors([4, self.height // 8, self.width // 8], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
x *= self.initial_noise_multiplier
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)