mirror of
https://github.com/vladmandic/sdnext.git
synced 2026-01-27 15:02:48 +03:00
462 lines
26 KiB
Python
462 lines
26 KiB
Python
import time
|
|
import inspect
|
|
import typing
|
|
import torch
|
|
import torchvision.transforms.functional as TF
|
|
import modules.devices as devices
|
|
import modules.shared as shared
|
|
import modules.sd_samplers as sd_samplers
|
|
import modules.sd_models as sd_models
|
|
import modules.sd_vae as sd_vae
|
|
import modules.taesd.sd_vae_taesd as sd_vae_taesd
|
|
import modules.images as images
|
|
from modules.lora_diffusers import lora_state, unload_diffusers_lora
|
|
from modules.processing import StableDiffusionProcessing
|
|
import modules.prompt_parser_diffusers as prompt_parser_diffusers
|
|
|
|
|
|
try:
|
|
import diffusers
|
|
except Exception as ex:
|
|
shared.log.error(f'Failed to import diffusers: {ex}')
|
|
|
|
|
|
def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_prompts):
|
|
results = []
|
|
if p.enable_hr and p.hr_upscaler != 'None' and p.denoising_strength > 0 and len(getattr(p, 'init_images', [])) == 0:
|
|
p.is_hr_pass = True
|
|
is_refiner_enabled = p.enable_hr and p.refiner_steps > 0 and p.refiner_start > 0 and p.refiner_start < 1 and shared.sd_refiner is not None
|
|
|
|
def hires_resize(latents): # input=latents output=pil
|
|
latent_upscaler = shared.latent_upscale_modes.get(p.hr_upscaler, None)
|
|
shared.log.info(f'Hires: upscaler={p.hr_upscaler} width={p.hr_upscale_to_x} height={p.hr_upscale_to_y} images={latents.shape[0]}')
|
|
if latent_upscaler is not None:
|
|
latents = torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
|
|
first_pass_images = vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil')
|
|
p.init_images = []
|
|
for first_pass_image in first_pass_images:
|
|
if latent_upscaler is None:
|
|
init_image = images.resize_image(1, first_pass_image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler)
|
|
else:
|
|
init_image = first_pass_image
|
|
# if is_refiner_enabled:
|
|
# init_image = vae_encode(init_image, model=shared.sd_model, full_quality=p.full_quality)
|
|
p.init_images.append(init_image)
|
|
return p.init_images
|
|
|
|
def save_intermediate(latents, suffix):
|
|
for i in range(len(latents)):
|
|
from modules.processing import create_infotext
|
|
info=create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, [], iteration=p.iteration, position_in_batch=i)
|
|
decoded = vae_decode(latents=latents, model=shared.sd_model, output_type='pil', full_quality=p.full_quality)
|
|
for j in range(len(decoded)):
|
|
images.save_image(decoded[j], path=p.outpath_samples, basename="", seed=seeds[i], prompt=prompts[i], extension=shared.opts.samples_format, info=info, p=p, suffix=suffix)
|
|
|
|
def diffusers_callback(_step: int, _timestep: int, latents: torch.FloatTensor):
|
|
shared.state.sampling_step += 1
|
|
shared.state.sampling_steps = p.steps
|
|
if p.is_hr_pass:
|
|
shared.state.sampling_steps += p.hr_second_pass_steps
|
|
shared.state.current_latent = latents
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
raise AssertionError('Interrupted...')
|
|
if shared.state.paused:
|
|
shared.log.debug('Sampling paused')
|
|
while shared.state.paused:
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
raise AssertionError('Interrupted...')
|
|
time.sleep(0.1)
|
|
|
|
def full_vae_decode(latents, model):
|
|
t0 = time.time()
|
|
if shared.opts.diffusers_move_unet and not model.has_accelerate:
|
|
shared.log.debug('Moving to CPU: model=UNet')
|
|
unet_device = model.unet.device
|
|
model.unet.to(devices.cpu)
|
|
devices.torch_gc()
|
|
if not shared.cmd_opts.lowvram and not shared.opts.diffusers_seq_cpu_offload:
|
|
model.vae.to(devices.device)
|
|
latents.to(model.vae.device)
|
|
|
|
upcast = (model.vae.dtype == torch.float16) and model.vae.config.force_upcast and hasattr(model, 'upcast_vae')
|
|
if upcast: # this is done by diffusers automatically if output_type != 'latent'
|
|
model.upcast_vae()
|
|
latents = latents.to(next(iter(model.vae.post_quant_conv.parameters())).dtype)
|
|
|
|
decoded = model.vae.decode(latents / model.vae.config.scaling_factor, return_dict=False)[0]
|
|
if shared.opts.diffusers_move_unet and not model.has_accelerate:
|
|
model.unet.to(unet_device)
|
|
t1 = time.time()
|
|
shared.log.debug(f'VAE decode: name={sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "baked"} dtype={model.vae.dtype} upcast={upcast} images={latents.shape[0]} latents={latents.shape} time={round(t1-t0, 3)}s')
|
|
return decoded
|
|
|
|
def full_vae_encode(image, model):
|
|
shared.log.debug(f'VAE encode: name={sd_vae.loaded_vae_file if sd_vae.loaded_vae_file is not None else "baked"} dtype={model.vae.dtype} upcast={model.vae.config.get("force_upcast", None)}')
|
|
if shared.opts.diffusers_move_unet and not model.has_accelerate:
|
|
shared.log.debug('Moving to CPU: model=UNet')
|
|
unet_device = model.unet.device
|
|
model.unet.to(devices.cpu)
|
|
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))
|
|
if shared.opts.diffusers_move_unet and not model.has_accelerate:
|
|
model.unet.to(unet_device)
|
|
return encoded
|
|
|
|
def taesd_vae_decode(latents):
|
|
shared.log.debug(f'VAE decode: name=TAESD images={len(latents)} latents={latents.shape}')
|
|
if len(latents) == 0:
|
|
return []
|
|
decoded = torch.zeros((len(latents), 3, latents.shape[2] * 8, latents.shape[3] * 8), dtype=devices.dtype_vae, device=devices.device)
|
|
for i in range(len(output.images)):
|
|
decoded[i] = (sd_vae_taesd.decode(latents[i]) * 2.0) - 1.0
|
|
return decoded
|
|
|
|
def taesd_vae_encode(image):
|
|
shared.log.debug(f'VAE encode: name=TAESD image={image.shape}')
|
|
encoded = sd_vae_taesd.encode(image)
|
|
return encoded
|
|
|
|
def vae_decode(latents, model, output_type='np', full_quality=True):
|
|
if not torch.is_tensor(latents): # already decoded
|
|
return latents
|
|
if latents.shape[0] == 0:
|
|
shared.log.error(f'VAE nothing to decode: {latents.shape}')
|
|
return []
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
return []
|
|
if not hasattr(model, 'vae'):
|
|
shared.log.error('VAE not found in model')
|
|
return []
|
|
if len(latents.shape) == 3: # lost a batch dim in hires
|
|
latents = latents.unsqueeze(0)
|
|
if full_quality:
|
|
decoded = full_vae_decode(latents=latents, model=shared.sd_model)
|
|
else:
|
|
decoded = taesd_vae_decode(latents=latents)
|
|
imgs = model.image_processor.postprocess(decoded, output_type=output_type)
|
|
return imgs
|
|
|
|
def vae_encode(image, model, full_quality=True): # pylint: disable=unused-variable
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
return []
|
|
if not hasattr(model, 'vae'):
|
|
shared.log.error('VAE not found in model')
|
|
return []
|
|
tensor = TF.to_tensor(image.convert("RGB")).unsqueeze(0).to(devices.device, devices.dtype_vae)
|
|
if full_quality:
|
|
latents = full_vae_encode(image=tensor, model=shared.sd_model)
|
|
else:
|
|
latents = taesd_vae_encode(image=tensor)
|
|
return latents
|
|
|
|
def fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2):
|
|
if type(prompts) is str:
|
|
prompts = [prompts]
|
|
if type(negative_prompts) is str:
|
|
negative_prompts = [negative_prompts]
|
|
while len(negative_prompts) < len(prompts):
|
|
negative_prompts.append(negative_prompts[-1])
|
|
if type(prompts_2) is str:
|
|
prompts_2 = [prompts_2]
|
|
if type(prompts_2) is list:
|
|
while len(prompts_2) < len(prompts):
|
|
prompts_2.append(prompts_2[-1])
|
|
if type(negative_prompts_2) is str:
|
|
negative_prompts_2 = [negative_prompts_2]
|
|
if type(negative_prompts_2) is list:
|
|
while len(negative_prompts_2) < len(prompts_2):
|
|
negative_prompts_2.append(negative_prompts_2[-1])
|
|
return prompts, negative_prompts, prompts_2, negative_prompts_2
|
|
|
|
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):
|
|
try:
|
|
is_refiner = model.text_encoder.__class__.__name__ != 'CLIPTextModel'
|
|
except Exception:
|
|
is_refiner = False
|
|
if hasattr(model, "set_progress_bar_config"):
|
|
model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba')
|
|
args = {}
|
|
signature = inspect.signature(type(model).__call__)
|
|
possible = signature.parameters.keys()
|
|
generator_device = devices.cpu if shared.opts.diffusers_generator_device == "cpu" else shared.device
|
|
generator = [torch.Generator(generator_device).manual_seed(s) for s in seeds]
|
|
prompt_embed = None
|
|
pooled = None
|
|
negative_embed = None
|
|
negative_pooled = None
|
|
prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
|
|
if shared.opts.prompt_attention in {'Compel parser', 'Full parser'} and 'StableDiffusion' in model.__class__.__name__:
|
|
prompt_embed, pooled, negative_embed, negative_pooled = prompt_parser_diffusers.compel_encode_prompts(model, prompts, negative_prompts, prompts_2, negative_prompts_2, is_refiner, kwargs.pop("clip_skip", None))
|
|
if 'prompt' in possible:
|
|
if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
|
|
if type(pooled) == list:
|
|
pooled = pooled[0]
|
|
if type(negative_pooled) == list:
|
|
negative_pooled = negative_pooled[0]
|
|
args['prompt_embeds'] = prompt_embed
|
|
if 'XL' in model.__class__.__name__:
|
|
args['pooled_prompt_embeds'] = pooled
|
|
else:
|
|
args['prompt'] = prompts
|
|
if 'negative_prompt' in possible:
|
|
if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and negative_embed is not None:
|
|
args['negative_prompt_embeds'] = negative_embed
|
|
if 'XL' in model.__class__.__name__:
|
|
args['negative_pooled_prompt_embeds'] = negative_pooled
|
|
else:
|
|
args['negative_prompt'] = negative_prompts
|
|
if 'guidance_scale' in possible:
|
|
args['guidance_scale'] = p.cfg_scale
|
|
if 'generator' in possible:
|
|
args['generator'] = generator
|
|
if 'output_type' in possible:
|
|
args['output_type'] = 'np'
|
|
if 'callback_steps' in possible:
|
|
args['callback_steps'] = 1
|
|
if 'callback' in possible:
|
|
args['callback'] = diffusers_callback
|
|
for arg in kwargs:
|
|
if arg in possible:
|
|
args[arg] = kwargs[arg]
|
|
else:
|
|
pass
|
|
# shared.log.debug(f'Diffuser not supported: pipeline={pipeline.__class__.__name__} task={sd_models.get_diffusers_task(model)} arg={arg}')
|
|
# shared.log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} possible={possible}')
|
|
clean = args.copy()
|
|
clean.pop('callback', None)
|
|
clean.pop('callback_steps', None)
|
|
if 'image' in clean:
|
|
clean['image'] = type(clean['image'])
|
|
if 'mask_image' in clean:
|
|
clean['mask_image'] = type(clean['mask_image'])
|
|
if 'prompt' in clean:
|
|
clean['prompt'] = len(clean['prompt'])
|
|
if 'negative_prompt' in clean:
|
|
clean['negative_prompt'] = len(clean['negative_prompt'])
|
|
if 'prompt_embeds' in clean:
|
|
clean['prompt_embeds'] = clean['prompt_embeds'].shape if torch.is_tensor(clean['prompt_embeds']) else type(clean['prompt_embeds'])
|
|
if 'pooled_prompt_embeds' in clean:
|
|
clean['pooled_prompt_embeds'] = clean['pooled_prompt_embeds'].shape if torch.is_tensor(clean['pooled_prompt_embeds']) else type(clean['pooled_prompt_embeds'])
|
|
if 'negative_prompt_embeds' in clean:
|
|
clean['negative_prompt_embeds'] = clean['negative_prompt_embeds'].shape if torch.is_tensor(clean['negative_prompt_embeds']) else type(clean['negative_prompt_embeds'])
|
|
if 'negative_pooled_prompt_embeds' in clean:
|
|
clean['negative_pooled_prompt_embeds'] = clean['negative_pooled_prompt_embeds'].shape if torch.is_tensor(clean['negative_pooled_prompt_embeds']) else type(clean['negative_pooled_prompt_embeds'])
|
|
clean['generator'] = generator_device
|
|
shared.log.debug(f'Diffuser pipeline: {model.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
|
|
return args
|
|
|
|
def recompile_model(hires=False):
|
|
if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
|
|
if shared.opts.cuda_compile_backend == "openvino_fx":
|
|
compile_height = p.height if not hires else p.hr_upscale_to_y
|
|
compile_width = p.width if not hires else p.hr_upscale_to_x
|
|
if (not hasattr(shared.sd_model, "compiled_model_state") or (not shared.sd_model.compiled_model_state.first_pass
|
|
and (shared.sd_model.compiled_model_state.height != compile_height or shared.sd_model.compiled_model_state.width != compile_width
|
|
or shared.sd_model.compiled_model_state.batch_size != p.batch_size))):
|
|
shared.log.info("OpenVINO: Resolution change detected")
|
|
shared.log.info("OpenVINO: Recompiling base model")
|
|
sd_models.unload_model_weights(op='model')
|
|
sd_models.reload_model_weights(op='model')
|
|
if is_refiner_enabled:
|
|
shared.log.info("OpenVINO: Recompiling refiner")
|
|
sd_models.unload_model_weights(op='refiner')
|
|
sd_models.reload_model_weights(op='refiner')
|
|
shared.sd_model.compiled_model_state.height = compile_height
|
|
shared.sd_model.compiled_model_state.width = compile_width
|
|
shared.sd_model.compiled_model_state.batch_size = p.batch_size
|
|
shared.sd_model.compiled_model_state.first_pass = False
|
|
else:
|
|
pass #Can be implemented for TensorRT or Olive
|
|
else:
|
|
pass #Do nothing if compile is disabled
|
|
|
|
is_karras_compatible = shared.sd_model.__class__.__init__.__annotations__.get("scheduler", None) == diffusers.schedulers.scheduling_utils.KarrasDiffusionSchedulers
|
|
use_sampler = p.sampler_name if not p.is_hr_pass else p.latent_sampler
|
|
if (not hasattr(shared.sd_model.scheduler, 'name')) or (shared.sd_model.scheduler.name != use_sampler) and (use_sampler != 'Default') and is_karras_compatible:
|
|
sampler = sd_samplers.all_samplers_map.get(use_sampler, None)
|
|
if sampler is None:
|
|
sampler = sd_samplers.all_samplers_map.get("UniPC")
|
|
sd_samplers.create_sampler(sampler.name, shared.sd_model) # TODO(Patrick): For wrapped pipelines this is currently a no-op
|
|
sampler_options = f'type:{shared.opts.schedulers_prediction_type} ' if shared.opts.schedulers_prediction_type != 'default' else ''
|
|
sampler_options += 'no_karras ' if not shared.opts.schedulers_use_karras else ''
|
|
sampler_options += 'no_low_order' if not shared.opts.schedulers_use_loworder else ''
|
|
sampler_options += 'dynamic_thresholding' if shared.opts.schedulers_use_thresholding else ''
|
|
sampler_options += f'solver:{shared.opts.schedulers_dpm_solver}' if shared.opts.schedulers_dpm_solver != 'sde-dpmsolver++' else ''
|
|
sampler_options += f'beta:{shared.opts.schedulers_beta_schedule}:{shared.opts.schedulers_beta_start}:{shared.opts.schedulers_beta_end}' if shared.opts.schedulers_beta_schedule != 'default' else ''
|
|
p.extra_generation_params['Sampler options'] = sampler_options if len(sampler_options) > 0 else None
|
|
p.extra_generation_params['Pipeline'] = shared.sd_model.__class__.__name__
|
|
|
|
cross_attention_kwargs={}
|
|
if len(getattr(p, 'init_images', [])) > 0:
|
|
while len(p.init_images) < len(prompts):
|
|
p.init_images.append(p.init_images[-1])
|
|
if lora_state['active']:
|
|
cross_attention_kwargs['scale'] = lora_state['multiplier']
|
|
task_specific_kwargs={}
|
|
if sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE:
|
|
p.ops.append('txt2img')
|
|
task_specific_kwargs = {"height": p.height, "width": p.width}
|
|
elif sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE:
|
|
p.ops.append('img2img')
|
|
task_specific_kwargs = {"image": p.init_images, "strength": p.denoising_strength}
|
|
elif sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING:
|
|
p.ops.append('inpaint')
|
|
task_specific_kwargs = {"image": p.init_images, "mask_image": p.mask, "strength": p.denoising_strength, "height": p.height, "width": p.width}
|
|
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
unload_diffusers_lora()
|
|
return results
|
|
|
|
recompile_model()
|
|
|
|
if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
|
|
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)
|
|
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)
|
|
|
|
def calculate_base_steps():
|
|
if use_refiner_start:
|
|
return int(p.steps // p.refiner_start + 1) if shared.sd_model_type == 'sdxl' else p.steps
|
|
elif use_denoise_start and shared.sd_model_type == 'sdxl':
|
|
return int(p.steps // (1 - p.refiner_start))
|
|
elif is_img2img:
|
|
return int(p.steps // p.denoising_strength + 1)
|
|
else:
|
|
return p.steps
|
|
|
|
base_args = set_pipeline_args(
|
|
model=shared.sd_model,
|
|
prompts=prompts,
|
|
negative_prompts=negative_prompts,
|
|
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
|
|
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
|
|
num_inference_steps=calculate_base_steps(),
|
|
eta=shared.opts.eta_ddim,
|
|
guidance_rescale=p.diffusers_guidance_rescale,
|
|
denoising_start=0 if use_refiner_start else p.refiner_start if use_denoise_start else None,
|
|
denoising_end=p.refiner_start if use_refiner_start else 1 if use_denoise_start else None,
|
|
output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
|
|
clip_skip=p.clip_skip,
|
|
desc='Base',
|
|
**task_specific_kwargs
|
|
)
|
|
p.extra_generation_params['CFG rescale'] = p.diffusers_guidance_rescale
|
|
p.extra_generation_params["Eta DDIM"] = shared.opts.eta_ddim if shared.opts.eta_ddim is not None and shared.opts.eta_ddim > 0 else None
|
|
try:
|
|
output = shared.sd_model(**base_args) # pylint: disable=not-callable
|
|
except AssertionError as e:
|
|
shared.log.info(e)
|
|
|
|
if hasattr(shared.sd_model, 'embedding_db') and len(shared.sd_model.embedding_db.embeddings_used) > 0:
|
|
p.extra_generation_params['Embeddings'] = ', '.join(shared.sd_model.embedding_db.embeddings_used)
|
|
|
|
if lora_state['active']:
|
|
p.extra_generation_params['LoRA method'] = shared.opts.diffusers_lora_loader
|
|
unload_diffusers_lora()
|
|
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
return results
|
|
|
|
# optional hires pass
|
|
latent_scale_mode = shared.latent_upscale_modes.get(p.hr_upscaler, None) if (hasattr(p, "hr_upscaler") and p.hr_upscaler is not None) else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
|
|
if p.is_hr_pass:
|
|
p.init_hr()
|
|
if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
|
|
p.ops.append('upscale')
|
|
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_highres_fix and hasattr(shared.sd_model, 'vae'):
|
|
save_intermediate(latents=output.images, suffix="-before-hires")
|
|
output.images = hires_resize(latents=output.images)
|
|
if latent_scale_mode is not None or p.hr_force:
|
|
p.ops.append('hires')
|
|
recompile_model(hires=True)
|
|
sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
|
|
hires_args = set_pipeline_args(
|
|
model=shared.sd_model,
|
|
prompts=prompts,
|
|
negative_prompts=negative_prompts,
|
|
prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
|
|
negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
|
|
num_inference_steps=int(p.hr_second_pass_steps // p.denoising_strength + 1),
|
|
eta=shared.opts.eta_ddim,
|
|
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
|
|
guidance_rescale=p.diffusers_guidance_rescale,
|
|
output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
|
|
clip_skip=p.clip_skip,
|
|
image=p.init_images,
|
|
strength=p.denoising_strength,
|
|
desc='Hires',
|
|
)
|
|
try:
|
|
output = shared.sd_model(**hires_args) # pylint: disable=not-callable
|
|
except AssertionError as e:
|
|
shared.log.info(e)
|
|
|
|
# optional refiner pass or decode
|
|
if is_refiner_enabled:
|
|
if shared.opts.save and not p.do_not_save_samples and shared.opts.save_images_before_refiner and hasattr(shared.sd_model, 'vae'):
|
|
save_intermediate(latents=output.images, suffix="-before-refiner")
|
|
if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
|
|
shared.log.debug('Moving to CPU: model=base')
|
|
shared.sd_model.to(devices.cpu)
|
|
devices.torch_gc()
|
|
|
|
if (not hasattr(shared.sd_refiner.scheduler, 'name')) or (shared.sd_refiner.scheduler.name != p.latent_sampler) and (p.sampler_name != 'Default'):
|
|
sampler = sd_samplers.all_samplers_map.get(p.latent_sampler, None)
|
|
if sampler is None:
|
|
sampler = sd_samplers.all_samplers_map.get("UniPC")
|
|
sd_samplers.create_sampler(sampler.name, shared.sd_refiner) # TODO(Patrick): For wrapped pipelines this is currently a no-op
|
|
|
|
if shared.state.interrupted or shared.state.skipped:
|
|
return results
|
|
|
|
if shared.opts.diffusers_move_refiner and not shared.sd_refiner.has_accelerate:
|
|
shared.sd_refiner.to(devices.device)
|
|
refiner_is_sdxl = bool("StableDiffusionXL" in shared.sd_refiner.__class__.__name__)
|
|
p.ops.append('refine')
|
|
for i in range(len(output.images)):
|
|
image = output.images[i]
|
|
# if (image.shape[2] == 3) and (image.shape[0] % 8 != 0 or image.shape[1] % 8 != 0):
|
|
# shared.log.warning(f'Refiner requires image size to be divisible by 8: {image.shape}')
|
|
# results.append(image)
|
|
# return results
|
|
refiner_args = set_pipeline_args(
|
|
model=shared.sd_refiner,
|
|
prompts=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts[i],
|
|
negative_prompts=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts[i],
|
|
num_inference_steps=int(p.refiner_steps // (1 - p.refiner_start)) if p.refiner_start > 0 and p.refiner_start < 1 and refiner_is_sdxl else int(p.refiner_steps // p.denoising_strength + 1) if refiner_is_sdxl else p.refiner_steps,
|
|
eta=shared.opts.eta_ddim,
|
|
strength=p.denoising_strength,
|
|
guidance_scale=p.image_cfg_scale if p.image_cfg_scale is not None else p.cfg_scale,
|
|
guidance_rescale=p.diffusers_guidance_rescale,
|
|
denoising_start=p.refiner_start if p.refiner_start > 0 and p.refiner_start < 1 else None,
|
|
denoising_end=1 if p.refiner_start > 0 and p.refiner_start < 1 else None,
|
|
image=image,
|
|
output_type='latent' if hasattr(shared.sd_refiner, 'vae') else 'np',
|
|
clip_skip=p.clip_skip,
|
|
desc='Refiner',
|
|
)
|
|
try:
|
|
refiner_output = shared.sd_refiner(**refiner_args) # pylint: disable=not-callable
|
|
except AssertionError as e:
|
|
shared.log.info(e)
|
|
|
|
if not shared.state.interrupted and not shared.state.skipped:
|
|
refiner_images = vae_decode(latents=refiner_output.images, model=shared.sd_refiner, full_quality=True)
|
|
for refiner_image in refiner_images:
|
|
results.append(refiner_image)
|
|
|
|
if shared.opts.diffusers_move_refiner and not shared.sd_refiner.has_accelerate:
|
|
shared.log.debug('Moving to CPU: model=refiner')
|
|
shared.sd_refiner.to(devices.cpu)
|
|
devices.torch_gc()
|
|
|
|
# final decode since there is no refiner
|
|
if not is_refiner_enabled:
|
|
results = vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality)
|
|
|
|
return results
|