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425 lines
24 KiB
Python
425 lines
24 KiB
Python
import time
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import inspect
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import typing
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import torch
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import numpy as np
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from PIL import Image
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import modules.devices as devices
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import modules.shared as shared
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import modules.sd_samplers as sd_samplers
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import modules.sd_models as sd_models
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import modules.sd_vae as sd_vae
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import modules.taesd.sd_vae_taesd as sd_vae_taesd
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import modules.images as images
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from modules.lora_diffusers import lora_state, unload_diffusers_lora
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from modules.processing import StableDiffusionProcessing
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import modules.prompt_parser_diffusers as prompt_parser_diffusers
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try:
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import diffusers
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except Exception as ex:
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shared.log.error(f'Failed to import diffusers: {ex}')
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def process_diffusers(p: StableDiffusionProcessing, seeds, prompts, negative_prompts):
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results = []
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if p.enable_hr and p.hr_upscaler != 'None' and p.denoising_strength > 0 and len(getattr(p, 'init_images', [])) == 0:
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p.is_hr_pass = True
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is_refiner_enabled = p.enable_hr and p.refiner_steps > 0 and shared.sd_refiner is not None
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def hires_resize(latents): # input=latents output=pil
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latent_upscaler = shared.latent_upscale_modes.get(p.hr_upscaler, None)
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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]}')
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if latent_upscaler is not None:
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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"])
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first_pass_images = vae_decode(latents=latents, model=shared.sd_model, full_quality=True, output_type='pil')
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p.init_images = []
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for first_pass_image in first_pass_images:
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init_image = images.resize_image(1, first_pass_image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler) if latent_upscaler is None else first_pass_image
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p.init_images.append(init_image)
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p.width = p.hr_upscale_to_x
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p.height = p.hr_upscale_to_y
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def save_intermediate(latents, suffix):
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for i in range(len(latents)):
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from modules.processing import create_infotext
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info=create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, [], iteration=p.iteration, position_in_batch=i)
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decoded = vae_decode(latents=latents, model=shared.sd_model, output_type='pil', full_quality=p.full_quality)
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for j in range(len(decoded)):
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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)
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def diffusers_callback(_step: int, _timestep: int, latents: torch.FloatTensor):
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shared.state.sampling_step += 1
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shared.state.sampling_steps = p.steps
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if p.is_hr_pass:
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shared.state.sampling_steps += p.hr_second_pass_steps
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shared.state.current_latent = latents
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if shared.state.interrupted or shared.state.skipped:
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raise AssertionError('Interrupted...')
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if shared.state.paused:
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shared.log.debug('Sampling paused')
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while shared.state.paused:
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if shared.state.interrupted or shared.state.skipped:
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raise AssertionError('Interrupted...')
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time.sleep(0.1)
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def full_vae_decode(latents, model):
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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={model.vae.config.get("force_upcast", None)} images={latents.shape[0]}')
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if shared.opts.diffusers_move_unet and not model.has_accelerate:
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shared.log.debug('Moving to CPU: model=UNet')
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unet_device = model.unet.device
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model.unet.to(devices.cpu)
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devices.torch_gc()
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if not shared.cmd_opts.lowvram and not shared.opts.diffusers_seq_cpu_offload:
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model.vae.to(devices.device)
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latents.to(model.vae.device)
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decoded = model.vae.decode(latents / model.vae.config.scaling_factor, return_dict=False)[0]
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if shared.opts.diffusers_move_unet and not model.has_accelerate:
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model.unet.to(unet_device)
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return decoded
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def taesd_vae_decode(latents):
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shared.log.debug(f'VAE decode: name=TAESD images={latents.shape[0]}')
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decoded = torch.zeros((len(latents), 3, p.height, p.width), dtype=devices.dtype_vae, device=devices.device)
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for i in range(len(output.images)):
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decoded[i] = (sd_vae_taesd.decode(latents[i]) * 2.0) - 1.0
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return decoded
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def vae_decode(latents, model, output_type='np', full_quality=True):
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if not torch.is_tensor(latents): # already decoded
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return latents
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if latents.shape[0] == 0:
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shared.log.error(f'VAE nothing to decode: {latents.shape}')
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return []
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if shared.state.interrupted or shared.state.skipped:
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return []
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if not hasattr(model, 'vae'):
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shared.log.error('VAE not found in model')
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return []
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if len(latents.shape) == 3: # lost a batch dim in hires
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latents = latents.unsqueeze(0)
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if full_quality:
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decoded = full_vae_decode(latents=latents, model=shared.sd_model)
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else:
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decoded = taesd_vae_decode(latents=latents)
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imgs = model.image_processor.postprocess(decoded, output_type=output_type)
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return imgs
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def fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2):
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if type(prompts) is str:
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prompts = [prompts]
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if type(negative_prompts) is str:
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negative_prompts = [negative_prompts]
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while len(negative_prompts) < len(prompts):
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negative_prompts.append(negative_prompts[-1])
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if type(prompts_2) is str:
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prompts_2 = [prompts_2]
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if type(prompts_2) is list:
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while len(prompts_2) < len(prompts):
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prompts_2.append(prompts_2[-1])
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if type(negative_prompts_2) is str:
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negative_prompts_2 = [negative_prompts_2]
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if type(negative_prompts_2) is list:
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while len(negative_prompts_2) < len(prompts_2):
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negative_prompts_2.append(negative_prompts_2[-1])
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return prompts, negative_prompts, prompts_2, negative_prompts_2
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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):
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try:
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is_refiner = model.text_encoder.__class__.__name__ != 'CLIPTextModel'
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except Exception:
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is_refiner = False
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if hasattr(model, "set_progress_bar_config"):
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model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining} '+desc, ncols=80, colour='#327fba')
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args = {}
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signature = inspect.signature(type(model).__call__)
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possible = signature.parameters.keys()
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generator_device = devices.cpu if shared.opts.diffusers_generator_device == "cpu" else shared.device
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generator = [torch.Generator(generator_device).manual_seed(s) for s in seeds]
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prompt_embed = None
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pooled = None
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negative_embed = None
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negative_pooled = None
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prompts, negative_prompts, prompts_2, negative_prompts_2 = fix_prompts(prompts, negative_prompts, prompts_2, negative_prompts_2)
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if shared.opts.prompt_attention in {'Compel parser', 'Full parser'}:
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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))
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if 'prompt' in possible:
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if hasattr(model, 'text_encoder') and 'prompt_embeds' in possible and prompt_embed is not None:
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if type(pooled) == list:
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pooled = pooled[0]
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if type(negative_pooled) == list:
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negative_pooled = negative_pooled[0]
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args['prompt_embeds'] = prompt_embed
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if 'XL' in model.__class__.__name__:
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args['pooled_prompt_embeds'] = pooled
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else:
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args['prompt'] = prompts
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if 'negative_prompt' in possible:
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if hasattr(model, 'text_encoder') and 'negative_prompt_embeds' in possible and negative_embed is not None:
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args['negative_prompt_embeds'] = negative_embed
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if 'XL' in model.__class__.__name__:
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args['negative_pooled_prompt_embeds'] = negative_pooled
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else:
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args['negative_prompt'] = negative_prompts
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if 'guidance_scale' in possible:
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args['guidance_scale'] = p.cfg_scale
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if 'generator' in possible:
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args['generator'] = generator
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if 'output_type' in possible:
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args['output_type'] = 'np'
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if 'callback_steps' in possible:
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args['callback_steps'] = 1
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if 'callback' in possible:
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args['callback'] = diffusers_callback
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for arg in kwargs:
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if arg in possible:
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args[arg] = kwargs[arg]
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else:
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pass
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# shared.log.debug(f'Diffuser not supported: pipeline={pipeline.__class__.__name__} task={sd_models.get_diffusers_task(model)} arg={arg}')
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# shared.log.debug(f'Diffuser pipeline: {pipeline.__class__.__name__} possible={possible}')
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clean = args.copy()
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clean.pop('callback', None)
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clean.pop('callback_steps', None)
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if 'image' in clean:
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clean['image'] = type(clean['image'])
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if 'mask_image' in clean:
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clean['mask_image'] = type(clean['mask_image'])
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if 'prompt' in clean:
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clean['prompt'] = len(clean['prompt'])
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if 'negative_prompt' in clean:
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clean['negative_prompt'] = len(clean['negative_prompt'])
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if 'prompt_embeds' in clean:
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clean['prompt_embeds'] = clean['prompt_embeds'].shape if torch.is_tensor(clean['prompt_embeds']) else type(clean['prompt_embeds'])
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if 'pooled_prompt_embeds' in clean:
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clean['pooled_prompt_embeds'] = clean['pooled_prompt_embeds'].shape if torch.is_tensor(clean['pooled_prompt_embeds']) else type(clean['pooled_prompt_embeds'])
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if 'negative_prompt_embeds' in clean:
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clean['negative_prompt_embeds'] = clean['negative_prompt_embeds'].shape if torch.is_tensor(clean['negative_prompt_embeds']) else type(clean['negative_prompt_embeds'])
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if 'negative_pooled_prompt_embeds' in clean:
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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'])
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clean['generator'] = generator_device
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shared.log.debug(f'Diffuser pipeline: {model.__class__.__name__} task={sd_models.get_diffusers_task(model)} set={clean}')
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return args
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def recompile_model(hires=False):
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
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if shared.opts.cuda_compile_backend == "openvino_fx":
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compile_height = p.height if not hires else p.hr_upscale_to_y
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compile_width = p.width if not hires else p.hr_upscale_to_x
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if (not hasattr(shared.sd_model, "compiled_model_state") or (not shared.sd_model.compiled_model_state.first_pass
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and (shared.sd_model.compiled_model_state.height != compile_height or shared.sd_model.compiled_model_state.width != compile_width
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or shared.sd_model.compiled_model_state.batch_size != p.batch_size))):
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shared.log.info("OpenVINO: Resolution change detected")
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shared.log.info("OpenVINO: Recompiling base model")
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sd_models.unload_model_weights(op='model')
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sd_models.reload_model_weights(op='model')
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if is_refiner_enabled:
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shared.log.info("OpenVINO: Recompiling refiner")
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sd_models.unload_model_weights(op='refiner')
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sd_models.reload_model_weights(op='refiner')
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shared.sd_model.compiled_model_state.height = compile_height
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shared.sd_model.compiled_model_state.width = compile_width
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shared.sd_model.compiled_model_state.batch_size = p.batch_size
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shared.sd_model.compiled_model_state.first_pass = False
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else:
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pass #Can be implemented for TensorRT or Olive
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else:
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pass #Do nothing if compile is disabled
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is_karras_compatible = shared.sd_model.__class__.__init__.__annotations__.get("scheduler", None) == diffusers.schedulers.scheduling_utils.KarrasDiffusionSchedulers
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use_sampler = p.sampler_name if not p.is_hr_pass else p.latent_sampler
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if (not hasattr(shared.sd_model.scheduler, 'name')) or (shared.sd_model.scheduler.name != use_sampler) and (use_sampler != 'Default') and is_karras_compatible:
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sampler = sd_samplers.all_samplers_map.get(use_sampler, None)
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if sampler is None:
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sampler = sd_samplers.all_samplers_map.get("UniPC")
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sd_samplers.create_sampler(sampler.name, shared.sd_model) # TODO(Patrick): For wrapped pipelines this is currently a no-op
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sampler_options = f'type:{shared.opts.schedulers_prediction_type} ' if shared.opts.schedulers_prediction_type != 'default' else ''
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sampler_options += 'no_karras ' if not shared.opts.schedulers_use_karras else ''
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sampler_options += 'no_low_order' if not shared.opts.schedulers_use_loworder else ''
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sampler_options += 'dynamic_thresholding' if shared.opts.schedulers_use_thresholding else ''
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sampler_options += f'solver:{shared.opts.schedulers_dpm_solver}' if shared.opts.schedulers_dpm_solver != 'sde-dpmsolver++' else ''
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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 ''
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p.extra_generation_params['Sampler options'] = sampler_options if len(sampler_options) > 0 else None
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p.extra_generation_params['Pipeline'] = shared.sd_model.__class__.__name__
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cross_attention_kwargs={}
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if len(getattr(p, 'init_images', [])) > 0:
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while len(p.init_images) < len(prompts):
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p.init_images.append(p.init_images[-1])
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if lora_state['active']:
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cross_attention_kwargs['scale'] = lora_state['multiplier']
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task_specific_kwargs={}
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if sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE:
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p.ops.append('txt2img')
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task_specific_kwargs = {"height": p.height, "width": p.width}
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elif sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE:
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p.ops.append('img2img')
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task_specific_kwargs = {"image": p.init_images, "strength": p.denoising_strength}
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elif sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING:
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p.ops.append('inpaint')
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task_specific_kwargs = {"image": p.init_images, "mask_image": p.mask, "strength": p.denoising_strength, "height": p.height, "width": p.width}
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if shared.state.interrupted or shared.state.skipped:
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unload_diffusers_lora()
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return results
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recompile_model()
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if shared.opts.diffusers_move_base and not shared.sd_model.has_accelerate:
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shared.sd_model.to(devices.device)
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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)
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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)
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use_denoise_start = bool(is_img2img and p.refiner_start > 0 and p.refiner_start < 1)
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def calculate_base_steps():
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if use_refiner_start:
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return int(p.steps // p.refiner_start + 1) if shared.sd_model_type == 'sdxl' else p.steps
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elif use_denoise_start and shared.sd_model_type == 'sdxl':
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return int(p.steps // (1 - p.refiner_start))
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elif is_img2img:
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return int(p.steps // p.denoising_strength + 1)
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else:
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return p.steps
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base_args = set_pipeline_args(
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model=shared.sd_model,
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prompts=prompts,
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negative_prompts=negative_prompts,
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prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
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negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
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num_inference_steps=calculate_base_steps(),
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eta=shared.opts.eta_ddim,
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guidance_rescale=p.diffusers_guidance_rescale,
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denoising_start=0 if use_refiner_start else p.refiner_start if use_denoise_start else None,
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denoising_end=p.refiner_start if use_refiner_start else 1 if use_denoise_start else None,
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output_type='latent' if hasattr(shared.sd_model, 'vae') else 'np',
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clip_skip=p.clip_skip,
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desc='Base',
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**task_specific_kwargs
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)
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p.extra_generation_params['CFG rescale'] = p.diffusers_guidance_rescale
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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
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try:
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output = shared.sd_model(**base_args) # pylint: disable=not-callable
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except AssertionError as e:
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shared.log.info(e)
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if hasattr(shared.sd_model, 'embedding_db') and len(shared.sd_model.embedding_db.embeddings_used) > 0:
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p.extra_generation_params['Embeddings'] = ', '.join(shared.sd_model.embedding_db.embeddings_used)
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if lora_state['active']:
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p.extra_generation_params['LoRA method'] = shared.opts.diffusers_lora_loader
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unload_diffusers_lora()
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if shared.state.interrupted or shared.state.skipped:
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return results
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# optional hires pass
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latent_scale_mode = shared.latent_upscale_modes.get(p.hr_upscaler, None) if p.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "None")
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if p.is_hr_pass:
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p.init_hr()
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if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
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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'):
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save_intermediate(latents=output.images, suffix="-before-hires")
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if latent_scale_mode is not None:
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p.ops.append('hires')
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recompile_model(hires=True)
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hires_resize(latents=output.images)
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sd_models.set_diffuser_pipe(shared.sd_model, sd_models.DiffusersTaskType.IMAGE_2_IMAGE)
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hires_args = set_pipeline_args(
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model=shared.sd_model,
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prompts=prompts,
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negative_prompts=negative_prompts,
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prompts_2=[p.refiner_prompt] if len(p.refiner_prompt) > 0 else prompts,
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negative_prompts_2=[p.refiner_negative] if len(p.refiner_negative) > 0 else negative_prompts,
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num_inference_steps=int(p.hr_second_pass_steps // p.denoising_strength + 1),
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|
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)):
|
|
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=output.images[i],
|
|
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()
|
|
|
|
if p.is_hr_pass and latent_scale_mode is None:
|
|
if p.width != p.hr_upscale_to_x or p.height != p.hr_upscale_to_y:
|
|
p.ops.append('upscale')
|
|
if not is_refiner_enabled:
|
|
results = vae_decode(latents=output.images, model=shared.sd_model, full_quality=p.full_quality)
|
|
upscaled = []
|
|
for image in results:
|
|
image = (image * 255.0).astype(np.uint8)
|
|
image = Image.fromarray(image)
|
|
image = images.resize_image(1, image, p.hr_upscale_to_x, p.hr_upscale_to_y, upscaler_name=p.hr_upscaler)
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
upscaled.append(image)
|
|
return upscaled
|
|
|
|
# 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
|