forked from Hitmare/Eris_api_tensor_patch
Update for 1.7.0
Update for the 1.7.0 version of the original file
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eb16857005
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28b331f4e8
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@ -142,7 +142,7 @@ class StableDiffusionProcessing:
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overlay_images: list = None
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eta: float = None
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do_not_reload_embeddings: bool = False
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denoising_strength: float = 0
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denoising_strength: float = None
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ddim_discretize: str = None
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s_min_uncond: float = None
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s_churn: float = None
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@ -298,7 +298,7 @@ class StableDiffusionProcessing:
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return conditioning
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def edit_image_conditioning(self, source_image):
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conditioning_image = images_tensor_to_samples(source_image*0.5+0.5, approximation_indexes.get(opts.sd_vae_encode_method))
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conditioning_image = shared.sd_model.encode_first_stage(source_image).mode()
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return conditioning_image
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@ -535,6 +535,7 @@ class Processed:
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self.all_seeds = all_seeds or p.all_seeds or [self.seed]
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self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
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self.infotexts = infotexts or [info]
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self.version = program_version()
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def js(self):
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obj = {
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@ -569,6 +570,7 @@ class Processed:
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"job_timestamp": self.job_timestamp,
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"clip_skip": self.clip_skip,
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"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
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"version": self.version,
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}
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return json.dumps(obj)
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@ -679,8 +681,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
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"Size": f"{p.width}x{p.height}",
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"Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
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"Model": p.sd_model_name if opts.add_model_name_to_info else None,
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"VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
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"VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
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"VAE hash": p.sd_vae_hash if opts.add_vae_hash_to_info else None,
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"VAE": p.sd_vae_name if opts.add_vae_name_to_info else None,
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"Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
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"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
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"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}"),
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@ -711,7 +713,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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if p.scripts is not None:
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p.scripts.before_process(p)
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stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
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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}
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try:
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# if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
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@ -799,7 +801,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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infotexts = []
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output_images = []
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with torch.no_grad(), p.sd_model.ema_scope():
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with devices.autocast():
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p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
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@ -873,7 +874,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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else:
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if opts.sd_vae_decode_method != 'Full':
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p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
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x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
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x_samples_ddim = torch.stack(x_samples_ddim).float()
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@ -886,6 +886,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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devices.torch_gc()
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state.nextjob()
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if p.scripts is not None:
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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@ -938,27 +940,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if opts.enable_pnginfo:
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image.info["parameters"] = text
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output_images.append(image)
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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]):
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image_mask = p.mask_for_overlay.convert('RGB')
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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')
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if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
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if opts.return_mask or opts.save_mask:
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image_mask = p.mask_for_overlay.convert('RGB')
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if save_samples and opts.save_mask:
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images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
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if opts.return_mask:
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output_images.append(image_mask)
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if opts.save_mask:
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images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
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if opts.save_mask_composite:
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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")
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if opts.return_mask:
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output_images.append(image_mask)
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if opts.return_mask_composite:
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output_images.append(image_mask_composite)
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if opts.return_mask_composite or opts.save_mask_composite:
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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')
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if save_samples and opts.save_mask_composite:
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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")
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if opts.return_mask_composite:
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output_images.append(image_mask_composite)
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del x_samples_ddim
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devices.torch_gc()
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state.nextjob()
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if not infotexts:
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infotexts.append(Processed(p, []).infotext(p, 0))
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p.color_corrections = None
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@ -1144,6 +1146,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if not self.enable_hr:
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return samples
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devices.torch_gc()
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if self.latent_scale_mode is None:
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decoded_samples = torch.stack(decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)).to(dtype=torch.float32)
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@ -1153,8 +1156,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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with sd_models.SkipWritingToConfig():
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sd_models.reload_model_weights(info=self.hr_checkpoint_info)
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devices.torch_gc()
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return self.sample_hr_pass(samples, decoded_samples, seeds, subseeds, subseed_strength, prompts)
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def sample_hr_pass(self, samples, decoded_samples, seeds, subseeds, subseed_strength, prompts):
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@ -1162,7 +1163,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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return samples
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self.is_hr_pass = True
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target_width = self.hr_upscale_to_x
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target_height = self.hr_upscale_to_y
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@ -1251,7 +1251,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
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self.is_hr_pass = False
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return decoded_samples
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def close(self):
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