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Eris_api_tensor_patch
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Update for 1.7.0 

New clean file of the 1.7.0 Version
This commit is contained in:
Hitmare 2023-12-23 15:07:33 +00:00
parent e0000cab58
commit f919025ac6
1 changed files with 1538 additions and 1539 deletions
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51
TRT-Patch/#Original/processing.py
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@ -142,7 +142,7 @@ class StableDiffusionProcessing:
overlay_images: list = None overlay_images: list = None
eta: float = None eta: float = None
do_not_reload_embeddings: bool = False do_not_reload_embeddings: bool = False
denoising_strength: float = 0 denoising_strength: float = None
ddim_discretize: str = None ddim_discretize: str = None
s_min_uncond: float = None s_min_uncond: float = None
s_churn: float = None s_churn: float = None
@ -296,7 +296,7 @@ class StableDiffusionProcessing:
return conditioning return conditioning
def edit_image_conditioning(self, source_image): def edit_image_conditioning(self, source_image):
conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method)) conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
return conditioning_image return conditioning_image
@ -533,6 +533,7 @@ class Processed:
self.all_seeds = all_seeds or p.all_seeds or [self.seed] self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed] self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info] self.infotexts = infotexts or [info]
self.version = program_version()
def js(self): def js(self):
obj = { obj = {
@ -567,6 +568,7 @@ class Processed:
"job_timestamp": self.job_timestamp, "job_timestamp": self.job_timestamp,
"clip_skip": self.clip_skip, "clip_skip": self.clip_skip,
"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning, "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
"version": self.version,
} }
return json.dumps(obj) return json.dumps(obj)
@ -677,8 +679,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Size": f"{p.width}x{p.height}", "Size": f"{p.width}x{p.height}",
"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None, "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
"Model": p.sd_model_name if opts.add_model_name_to_info else None, "Model": p.sd_model_name if opts.add_model_name_to_info else None,
"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None, "VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
"VAE": p.sd_vae_name if opts.add_model_name_to_info else None, "VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])), "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
"Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@ -709,7 +711,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None: if p.scripts is not None:
p.scripts.before_process(p) p.scripts.before_process(p)
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()} stored_opts = {k: opts.data[k] if k in opts.data else opts.get_default(k) for k in p.override_settings.keys() if k in opts.data}
try: try:
# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
@ -797,7 +799,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
infotexts = [] infotexts = []
output_images = [] output_images = []
with torch.no_grad(), p.sd_model.ema_scope(): with torch.no_grad(), p.sd_model.ema_scope():
with devices.autocast(): with devices.autocast():
p.init(p.all_prompts, p.all_seeds, p.all_subseeds) p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
@ -871,7 +872,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else: else:
if opts.sd_vae_decode_method != 'Full': if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True) x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
x_samples_ddim = torch.stack(x_samples_ddim).float() x_samples_ddim = torch.stack(x_samples_ddim).float()
@ -884,6 +884,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc() devices.torch_gc()
state.nextjob()
if p.scripts is not None: if p.scripts is not None:
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n) p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
@ -936,27 +938,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if opts.enable_pnginfo: if opts.enable_pnginfo:
image.info["parameters"] = text image.info["parameters"] = text
output_images.append(image) output_images.append(image)
if save_samples and hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]): if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
image_mask = p.mask_for_overlay.convert('RGB') if opts.return_mask or opts.save_mask:
image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA') image_mask = p.mask_for_overlay.convert('RGB')
if save_samples and opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
if opts.return_mask:
output_images.append(image_mask)
if opts.save_mask: if opts.return_mask_composite or opts.save_mask_composite:
images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask") image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if save_samples and opts.save_mask_composite:
if opts.save_mask_composite: images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite") if opts.return_mask_composite:
output_images.append(image_mask_composite)
if opts.return_mask:
output_images.append(image_mask)
if opts.return_mask_composite:
output_images.append(image_mask_composite)
del x_samples_ddim del x_samples_ddim
devices.torch_gc() devices.torch_gc()
state.nextjob() if not infotexts:
infotexts.append(Processed(p, []).infotext(p, 0))
p.color_corrections = None p.color_corrections = None
@ -1142,6 +1144,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not self.enable_hr: if not self.enable_hr:
return samples return samples
devices.torch_gc()
if self.latent_scale_mode is None: if self.latent_scale_mode is None:
decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32) decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
@ -1151,8 +1154,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
with sd_models.SkipWritingToConfig(): with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=self.hr_checkpoint_info) sd_models.reload_model_weights(info=self.hr_checkpoint_info)
devices.torch_gc()
return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts) return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts): def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
@ -1160,7 +1161,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
return samples return samples
self.is_hr_pass = True self.is_hr_pass = True
target_width = self.hr_upscale_to_x target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y target_height = self.hr_upscale_to_y
@ -1249,7 +1249,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True) decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
self.is_hr_pass = False self.is_hr_pass = False
return decoded_samples return decoded_samples
def close(self): def close(self):