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rename latent sampler

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Vladimir Mandic
2024-01-02 08:14:17 -05:00
parent 547e7dfb0a
commit dc9778b701
13 changed files with 37 additions and 37 deletions
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modules/processing.py
+8 -8
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@@ -135,7 +135,7 @@ class StableDiffusionProcessing:
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, latent_sampler: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, image_cfg_scale: float = None, clip_skip: int = 1, width: int = 512, height: int = 512, full_quality: bool = True, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, diffusers_guidance_rescale: float = 0.7, resize_mode: int = 0, resize_name: str = 'None', scale_by: float = 0, selected_scale_tab: int = 0, hdr_clamp: bool = False, hdr_boundary: float = 4.0, hdr_threshold: float = 3.5, hdr_center: bool = False, hdr_channel_shift: float = 0.8, hdr_full_shift: float = 0.8, hdr_maximize: bool = False, hdr_max_center: float = 0.6, hdr_max_boundry: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): # pylint: disable=unused-argument
def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, hr_sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, image_cfg_scale: float = None, clip_skip: int = 1, width: int = 512, height: int = 512, full_quality: bool = True, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, diffusers_guidance_rescale: float = 0.7, resize_mode: int = 0, resize_name: str = 'None', scale_by: float = 0, selected_scale_tab: int = 0, hdr_clamp: bool = False, hdr_boundary: float = 4.0, hdr_threshold: float = 3.5, hdr_center: bool = False, hdr_channel_shift: float = 0.8, hdr_full_shift: float = 0.8, hdr_maximize: bool = False, hdr_max_center: float = 0.6, hdr_max_boundry: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None): # pylint: disable=unused-argument
self.outpath_samples: str = outpath_samples
self.outpath_grids: str = outpath_grids
self.prompt: str = prompt
@@ -148,7 +148,7 @@ class StableDiffusionProcessing:
self.seed_resize_from_h: int = seed_resize_from_h
self.seed_resize_from_w: int = seed_resize_from_w
self.sampler_name: str = sampler_name
self.latent_sampler: str = latent_sampler
self.hr_sampler_name: str = hr_sampler_name
self.batch_size: int = batch_size
self.n_iter: int = n_iter
self.steps: int = steps
@@ -633,7 +633,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
args["Hires resize"] = f"{p.hr_resize_x}x{p.hr_resize_y}"
args["Hires size"] = f"{p.hr_upscale_to_x}x{p.hr_upscale_to_y}"
args["Denoising strength"] = p.denoising_strength
args["Latent sampler"] = p.latent_sampler
args["Hires sampler"] = p.hr_sampler_name
args["Image CFG scale"] = p.image_cfg_scale
args["CFG rescale"] = p.diffusers_guidance_rescale
if 'refine' in p.ops:
@@ -643,7 +643,7 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
args['Refiner steps'] = p.refiner_steps
args['Refiner start'] = p.refiner_start
args["Hires steps"] = p.hr_second_pass_steps
args["Latent sampler"] = p.latent_sampler
args["Hires sampler"] = p.hr_sampler_name
args["CFG rescale"] = p.diffusers_guidance_rescale
if 'img2img' in p.ops or 'inpaint' in p.ops:
args["Init image size"] = f"{getattr(p, 'init_img_width', 0)}x{getattr(p, 'init_img_height', 0)}"
@@ -1129,7 +1129,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.is_hr_pass = True
hypertile_set(self, hr=True)
shared.state.job_count = 2 * self.n_iter
shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.latent_sampler}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
shared.log.debug(f'Init hires: upscaler="{self.hr_upscaler}" sampler="{self.hr_sampler_name}" resize={self.hr_resize_x}x{self.hr_resize_y} upscale={self.hr_upscale_to_x}x{self.hr_upscale_to_y}')
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
@@ -1198,14 +1198,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples.to(dtype=devices.dtype_vae), self.full_quality), samples)
else:
image_conditioning = self.txt2img_image_conditioning(samples.to(dtype=devices.dtype_vae))
if self.latent_sampler == "PLMS":
self.latent_sampler = 'UniPC'
if self.hr_sampler_name == "PLMS":
self.hr_sampler_name = 'UniPC'
if self.hr_force or latent_scale_mode is not None:
shared.state.job = 'hires'
if self.denoising_strength > 0:
self.ops.append('hires')
devices.torch_gc() # GC now before running the next img2img to prevent running out of memory
self.sampler = modules.sd_samplers.create_sampler(self.latent_sampler or self.sampler_name, self.sd_model)
self.sampler = modules.sd_samplers.create_sampler(self.hr_sampler_name or self.sampler_name, self.sd_model)
if hasattr(self.sampler, "initialize"):
self.sampler.initialize(self)
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]