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work on taesd encode

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Vladimir Mandic
2023-10-31 18:41:14 -04:00
parent ec0d49acb7
commit 072b870b3f
4 changed files with 22 additions and 6 deletions
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modules/processing_diffusers.py
+16 -3
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@@ -14,7 +14,7 @@ import modules.sd_vae as sd_vae
import modules.taesd.sd_vae_taesd as sd_vae_taesd
import modules.images as images
import modules.errors as errors
from modules.processing import StableDiffusionProcessing
from modules.processing import StableDiffusionProcessing, create_random_tensors
import modules.prompt_parser_diffusers as prompt_parser_diffusers
from modules.sd_hijack_hypertile import hypertile_set
@@ -105,7 +105,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
devices.torch_gc()
if not shared.cmd_opts.lowvram and not shared.opts.diffusers_seq_cpu_offload:
model.vae.to(devices.device)
encoded = model.vae.encode(image.to(model.vae.device, model.vae.dtype))
encoded = model.vae.encode(image.to(model.vae.device, model.vae.dtype)).latent_dist.sample()
if shared.opts.diffusers_move_unet and not getattr(model, 'has_accelerate', False):
model.unet.to(unet_device)
return encoded
@@ -147,6 +147,9 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
shared.state.job = prev_job
return imgs
def t(x):
return f"\033[34m{str(tuple(x.shape)).ljust(24)}\033[0m (\033[31mmin {x.amin().item():+.4f}\033[0m / \033[32mmean {x.mean().item():+.4f}\033[0m / \033[33mmax {x.amax().item():+.4f}\033[0m)"
def vae_encode(image, model, full_quality=True): # pylint: disable=unused-variable
if shared.state.interrupted or shared.state.skipped:
return []
@@ -155,6 +158,7 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
return []
tensor = TF.to_tensor(image.convert("RGB")).unsqueeze(0).to(devices.device, devices.dtype_vae)
if full_quality:
tensor = tensor * 2 - 1
latents = full_vae_encode(image=tensor, model=shared.sd_model)
else:
latents = taesd_vae_encode(image=tensor)
@@ -198,6 +202,12 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
width = 8 * math.ceil(p.init_images[0].width / 8)
height = 8 * math.ceil(p.init_images[0].height / 8)
task_args = {"image": p.init_images, "mask_image": p.mask, "strength": p.denoising_strength, "height": height, "width": width}
if model.__class__.__name__ == 'LatentConsistencyModelPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
init_latents = [vae_encode(image, model=shared.sd_model, full_quality=p.full_quality).squeeze(dim=0) for image in p.init_images]
init_latent = torch.stack(init_latents, dim=0).to(shared.device)
init_noise = p.denoising_strength * create_random_tensors(init_latent.shape[1:], seeds=p.all_seeds, subseeds=p.all_subseeds, subseed_strength=p.subseed_strength, p=p)
init_latent = (1 - p.denoising_strength) * init_latent + init_noise
task_args = {"latents": init_latent.to(model.dtype), "width": p.width, "height": p.height }
return task_args
def set_pipeline_args(model, prompts: list, negative_prompts: list, prompts_2: typing.Optional[list]=None, negative_prompts_2: typing.Optional[list]=None, desc:str='', **kwargs):
@@ -268,6 +278,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
clean = args.copy()
clean.pop('callback', None)
clean.pop('callback_steps', None)
if 'latents' in clean:
clean['latents'] = clean['latents'].shape
if 'image' in clean:
clean['image'] = type(clean['image'])
if 'mask_image' in clean:
@@ -343,7 +355,8 @@ def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_pro
if shared.opts.diffusers_move_base and not getattr(shared.sd_model, 'has_accelerate', False):
shared.sd_model.to(devices.device)
is_img2img = bool(sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
is_img2img = bool(sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or
sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING)
use_refiner_start = bool(is_refiner_enabled and not p.is_hr_pass and not is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
use_denoise_start = bool(is_img2img and p.refiner_start > 0 and p.refiner_start < 1)