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refactor backend detection

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This commit is contained in:
Vladimir Mandic
2024-06-06 20:41:10 -04:00
parent c8d6f03209
commit 5574833f0d
55 changed files with 171 additions and 170 deletions
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modules/processing_class.py
+9 -9
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@@ -215,7 +215,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.script_args = []
def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
if shared.backend == shared.Backend.DIFFUSERS:
if shared.native:
shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
self.width = self.width or 512
self.height = self.height or 512
@@ -252,7 +252,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_upscale_to_y = self.hr_resize_y
self.truncate_x = (self.hr_upscale_to_x - target_w) // 8
self.truncate_y = (self.hr_upscale_to_y - target_h) // 8
if shared.backend == shared.Backend.ORIGINAL: # diffusers are handled in processing_diffusers
if not shared.native: # diffusers are handled in processing_diffusers
if (self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height) or upscaler is None or upscaler == 'None': # special case: the user has chosen to do nothing
self.is_hr_pass = False
return
@@ -303,9 +303,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.script_args = []
def init(self, all_prompts=None, all_seeds=None, all_subseeds=None):
if shared.backend == shared.Backend.DIFFUSERS and getattr(self, 'image_mask', None) is not None:
if shared.native and getattr(self, 'image_mask', None) is not None:
shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.INPAINTING)
elif shared.backend == shared.Backend.DIFFUSERS and getattr(self, 'init_images', None) is not None:
elif shared.native and getattr(self, 'init_images', None) is not None:
shared.sd_model = sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
if all_prompts is not None:
@@ -317,7 +317,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if self.sampler_name == "PLMS":
self.sampler_name = 'UniPC'
if shared.backend == shared.Backend.ORIGINAL:
if not shared.native:
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
if hasattr(self.sampler, "initialize"):
self.sampler.initialize(self)
@@ -331,7 +331,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
if self.image_mask is not None:
if type(self.image_mask) == list:
self.image_mask = self.image_mask[0]
if shared.backend == shared.Backend.ORIGINAL: # original way of processing mask
if not shared.native: # original way of processing mask
self.image_mask = processing_helpers.create_binary_mask(self.image_mask)
if self.inpainting_mask_invert:
self.image_mask = ImageOps.invert(self.image_mask)
@@ -341,7 +341,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur)
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur)
self.image_mask = Image.fromarray(np_mask)
elif shared.backend == shared.Backend.DIFFUSERS:
elif shared.native:
if 'control' in self.ops:
self.image_mask = masking.run_mask(input_image=self.init_images, input_mask=self.image_mask, return_type='Grayscale', invert=self.inpainting_mask_invert==1) # blur/padding are handled in masking module
else:
@@ -411,9 +411,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.overlay_images = self.overlay_images * self.batch_size
if self.color_corrections is not None and len(self.color_corrections) == 1:
self.color_corrections = self.color_corrections * self.batch_size
if shared.backend == shared.Backend.DIFFUSERS:
if shared.native:
return # we've already set self.init_images and self.mask and we dont need any more processing
elif shared.backend == shared.Backend.ORIGINAL:
elif not shared.native:
self.init_images = [np.moveaxis((np.array(image).astype(np.float32) / 255.0), 2, 0) for image in self.init_images]
if len(self.init_images) == 1:
batch_images = np.expand_dims(self.init_images[0], axis=0).repeat(self.batch_size, axis=0)