add infotext (aa744cad) · Commits · github_fork / Stable Diffusion Webui

modules/generation_parameters_copypaste.py

+8 −0

Original line number	Diff line number	Diff line
		@@ -304,6 +304,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
		if "Schedule rho" not in res:
		res["Schedule rho"] = 0

		if "VAE Encoder" not in res:
		res["VAE Encoder"] = "Full"

		if "VAE Decoder" not in res:
		res["VAE Decoder"] = "Full"

		return res


		@@ -329,6 +335,8 @@ infotext_to_setting_name_mapping = [
		('RNG', 'randn_source'),
		('NGMS', 's_min_uncond'),
		('Pad conds', 'pad_cond_uncond'),
		('VAE Encoder', 'sd_vae_encode_method'),
		('VAE Decoder', 'sd_vae_decode_method'),
		]

modules/processing.py

+3 −0

Original line number	Diff line number	Diff line
		@@ -788,6 +788,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
		samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)

		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 = torch.stack(x_samples_ddim).float()
		x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
		@@ -1100,6 +1101,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		decoded_samples = torch.from_numpy(np.array(batch_images))
		decoded_samples = decoded_samples.to(shared.device)

		self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
		samples = images_tensor_to_samples(decoded_samples, approximation_indexes.get(opts.sd_vae_encode_method))

		image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
		@@ -1338,6 +1340,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")

		image = torch.from_numpy(batch_images)
		self.extra_generation_params['VAE Encoder'] = opts.sd_vae_encode_method
		self.init_latent = images_tensor_to_samples(image, approximation_indexes.get(opts.sd_vae_encode_method), self.sd_model)
		devices.torch_gc()

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