mirror of https://github.com/vladmandic/automatic
454 lines
23 KiB
Python
454 lines
23 KiB
Python
import os
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import re
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import math
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import time
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import inspect
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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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from modules import shared, sd_models, processing, processing_vae, processing_helpers, sd_hijack_hypertile, extra_networks, sd_vae
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from modules.logger import log
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from modules.processing_callbacks import diffusers_callback_legacy, diffusers_callback, set_callbacks_p
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from modules.processing_helpers import get_generator, apply_circular # pylint: disable=unused-import
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from modules.processing_prompt import set_prompt
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from modules.api import helpers
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debug_enabled = os.environ.get('SD_DIFFUSERS_DEBUG', None)
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debug_log = log.trace if debug_enabled else lambda *args, **kwargs: None
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disable_pbar = os.environ.get('SD_DISABLE_PBAR', None) is not None
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def task_modular_kwargs(p, model): # pylint: disable=unused-argument
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# model_cls = model.__class__.__name__
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task_args = {}
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p.ops.append('modular')
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processing_helpers.resize_init_images(p)
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task_args['width'] = p.width
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task_args['height'] = p.height
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if len(getattr(p, 'init_images', [])) > 0:
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task_args['image'] = p.init_images
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task_args['strength'] = p.denoising_strength
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mask_image = p.task_args.get('image_mask', None) or getattr(p, 'image_mask', None) or getattr(p, 'mask', None)
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if mask_image is not None:
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task_args['mask_image'] = mask_image
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if debug_enabled:
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debug_log(f'Process task specific args: {task_args}')
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return task_args
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def task_specific_kwargs(p, model):
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model_cls = model.__class__.__name__
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vae_scale_factor = sd_vae.get_vae_scale_factor(model)
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task_args = {}
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is_img2img_model = bool('Zero123' in model_cls)
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task_type = sd_models.get_diffusers_task(model)
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if len(getattr(p, 'init_images', [])) > 0:
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if isinstance(p.init_images[0], str):
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p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images]
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if isinstance(p.init_images[0], Image.Image):
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p.init_images = [i.convert('RGB') if i.mode != 'RGB' else i for i in p.init_images if i is not None]
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width, height = processing_helpers.resize_init_images(p)
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if (task_type == sd_models.DiffusersTaskType.TEXT_2_IMAGE or len(getattr(p, 'init_images', [])) == 0) and not is_img2img_model and 'video' not in p.ops:
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p.ops.append('txt2img')
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if hasattr(p, 'width') and hasattr(p, 'height'):
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task_args = {
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'width': width,
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'height': height,
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}
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elif (task_type == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
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if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
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if model_cls in sd_models.i2i_pipes:
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pass
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else:
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model.register_to_config(requires_aesthetics_score = False)
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if 'hires' not in p.ops:
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p.ops.append('img2img')
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if p.vae_type == 'Remote':
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from modules.vae.sd_vae_remote import remote_encode
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p.init_images = remote_encode(p.init_images)
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task_args = {
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'image': p.init_images,
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'strength': p.denoising_strength,
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}
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if model_cls == 'FluxImg2ImgPipeline' or model_cls == 'FluxKontextPipeline': # needs explicit width/height
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if torch.is_tensor(p.init_images[0]):
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p.width = p.init_images[0].shape[-1] * vae_scale_factor
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p.height = p.init_images[0].shape[-2] * vae_scale_factor
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else:
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p.width = width
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p.height = height
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if model_cls == 'FluxKontextPipeline':
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aspect_ratio = p.width / p.height
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max_area = max(p.width, p.height)**2
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p.width = round((max_area * aspect_ratio) ** 0.5)
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p.height = round((max_area / aspect_ratio) ** 0.5)
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p.width = p.width // vae_scale_factor * vae_scale_factor
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p.height = p.height // vae_scale_factor * vae_scale_factor
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task_args['max_area'] = max_area
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task_args['width'], task_args['height'] = p.width, p.height
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elif model_cls == 'OmniGenPipeline' or model_cls == 'OmniGen2Pipeline':
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p.width = width
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p.height = height
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task_args = {
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'width': p.width,
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'height': p.height,
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'input_images': [p.init_images], # omnigen expects list-of-lists
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}
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elif task_type == sd_models.DiffusersTaskType.INSTRUCT and len(getattr(p, 'init_images', [])) > 0:
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p.ops.append('instruct')
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task_args = {
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'width': width if hasattr(p, 'width') else None,
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'height': height if hasattr(p, 'height') else None,
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'image': p.init_images,
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'strength': p.denoising_strength,
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}
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elif (task_type == sd_models.DiffusersTaskType.INPAINTING or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
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if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
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if model_cls in [sd_models.i2i_pipes]:
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pass
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else:
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model.register_to_config(requires_aesthetics_score = False)
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if p.detailer_enabled:
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p.ops.append('detailer')
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else:
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p.ops.append('inpaint')
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mask_image = p.task_args.get('image_mask', None) or getattr(p, 'image_mask', None) or getattr(p, 'mask', None)
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if p.vae_type == 'Remote':
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from modules.vae.sd_vae_remote import remote_encode
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p.init_images = remote_encode(p.init_images)
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# mask_image = remote_encode(mask_image)
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task_args = {
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'image': p.init_images,
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'mask_image': mask_image,
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'strength': p.denoising_strength,
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'height': height,
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'width': width,
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}
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# model specific args
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if ('QwenImageEdit' in model_cls) and (p.init_images is None or len(p.init_images) == 0):
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task_args['image'] = [Image.new('RGB', (p.width, p.height), (0, 0, 0))] # monkey-patch so qwen-image-edit pipeline does not error-out on t2i
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if ('QwenImageEditPlusPipeline' in model_cls) and (p.init_control is not None) and (len(p.init_control) > 0):
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task_args['image'] += p.init_control
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if ('QwenImageLayeredPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['image'] = p.init_images[0].convert('RGBA')
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if ('Flux2' in model_cls) and (p.init_control is not None) and (len(p.init_control) > 0):
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task_args['image'] += p.init_control
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if ('LatentConsistencyModelPipeline' in model_cls) and (len(p.init_images) > 0):
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p.ops.append('lcm')
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init_latents = [processing_vae.vae_encode(image, model=shared.sd_model, vae_type=p.vae_type).squeeze(dim=0) for image in p.init_images]
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init_latent = torch.stack(init_latents, dim=0).to(shared.device)
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init_noise = p.denoising_strength * processing.create_random_tensors(init_latent.shape[1:], seeds=p.all_seeds, subseeds=p.all_subseeds, subseed_strength=p.subseed_strength, p=p)
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init_latent = (1 - p.denoising_strength) * init_latent + init_noise
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task_args = {
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'latents': init_latent.to(model.dtype),
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'width': p.width,
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'height': p.height,
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}
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if ('WanImageToVideoPipeline' in model_cls) or ('ChronoEditPipeline' in model_cls):
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if (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['image'] = p.init_images[0]
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else:
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task_args['image'] = Image.new('RGB', (p.width, p.height), (0, 0, 0)) # monkey-patch so wan-i2i pipeline does not error-out on t2i
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if ('WanVACEPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['reference_images'] = p.init_images
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if ('GoogleNanoBananaPipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['image'] = p.init_images[0]
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if ('GlmImagePipeline' in model_cls) and (p.init_images is not None) and (len(p.init_images) > 0):
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task_args['image'] = p.init_images
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if 'BlipDiffusionPipeline' in model_cls:
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if len(p.init_images) == 0:
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log.error('BLiP diffusion requires init image')
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return task_args
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task_args = {
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'reference_image': p.init_images[0],
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'source_subject_category': getattr(p, 'negative_prompt', '').split()[-1],
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'target_subject_category': getattr(p, 'prompt', '').split()[-1],
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'output_type': 'pil',
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}
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if debug_enabled:
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debug_log(f'Process task specific args: {task_args}')
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return task_args
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def get_params(model):
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if hasattr(model, 'blocks') and hasattr(model.blocks, 'inputs'): # modular pipeline
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possible = [input_param.name for input_param in model.blocks.inputs]
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return possible
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else:
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signature = inspect.signature(type(model).__call__, follow_wrapped=True)
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possible = list(signature.parameters)
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return possible
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def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:list | None=None, negative_prompts_2:list | None=None, prompt_attention:str | None=None, desc:str | None='', **kwargs):
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t0 = time.time()
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
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argsid = shared.state.begin('Params')
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apply_circular(p.tiling, model)
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args = {}
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has_vae = hasattr(model, 'vae') or (hasattr(model, 'pipe') and hasattr(model.pipe, 'vae'))
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cls = model.__class__.__name__
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if hasattr(model, 'pipe') and not hasattr(model, 'no_recurse'): # recurse
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model = model.pipe
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has_vae = has_vae or hasattr(model, 'vae')
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if hasattr(model, "set_progress_bar_config"):
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if disable_pbar:
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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} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba', disable=disable_pbar)
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else:
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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} ' + '\x1b[38;5;71m' + desc, ncols=80, colour='#327fba')
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possible = get_params(model)
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if debug_enabled:
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debug_log(f'Process pipeline possible: {possible}')
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steps = kwargs.get("num_inference_steps", None) or len(getattr(p, 'timesteps', ['1']))
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clip_skip = kwargs.pop("clip_skip", 1)
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prompt_attention, args = set_prompt(p, args, possible, cls, prompt_attention, steps, clip_skip, prompts, negative_prompts, prompts_2, negative_prompts_2)
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if 'clip_skip' in possible:
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if clip_skip == 1:
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pass # clip_skip = None
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else:
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args['clip_skip'] = clip_skip - 1
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if shared.opts.lora_apply_te:
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extra_networks.activate(p, include=['text_encoder', 'text_encoder_2', 'text_encoder_3'])
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if 'complex_human_instruction' in possible:
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chi = shared.opts.te_complex_human_instruction
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p.extra_generation_params["CHI"] = chi
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if not chi:
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args['complex_human_instruction'] = None
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if 'use_resolution_binning' in possible:
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args['use_resolution_binning'] = False
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if 'use_mask_in_transformer' in possible:
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args['use_mask_in_transformer'] = shared.opts.te_use_mask
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sched_timesteps = getattr(p, 'schedulers_timesteps', None)
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if sched_timesteps is None:
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sched_timesteps = shared.opts.schedulers_timesteps
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timesteps = re.split(',| ', sched_timesteps)
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if len(timesteps) > 2:
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if ('timesteps' in possible) and hasattr(model.scheduler, 'set_timesteps') and ("timesteps" in set(inspect.signature(model.scheduler.set_timesteps).parameters.keys())):
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p.timesteps = [int(x) for x in timesteps if x.isdigit()]
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p.steps = len(timesteps)
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args['timesteps'] = p.timesteps
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log.debug(f'Sampler: steps={len(p.timesteps)} timesteps={p.timesteps}')
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elif ('sigmas' in possible) and hasattr(model.scheduler, 'set_timesteps') and ("sigmas" in set(inspect.signature(model.scheduler.set_timesteps).parameters.keys())):
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p.timesteps = [float(x)/1000.0 for x in timesteps if x.isdigit()]
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p.steps = len(p.timesteps)
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args['sigmas'] = p.timesteps
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log.debug(f'Sampler: steps={len(p.timesteps)} sigmas={p.timesteps}')
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else:
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log.warning(f'Sampler: cls={model.scheduler.__class__.__name__} timesteps not supported')
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if hasattr(model, 'scheduler') and hasattr(model.scheduler, 'noise_sampler_seed') and hasattr(model.scheduler, 'noise_sampler'):
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model.scheduler.noise_sampler = None # noise needs to be reset instead of using cached values
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model.scheduler.noise_sampler_seed = p.seeds # some schedulers have internal noise generator and do not use pipeline generator
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if 'seed' in possible and p.seed is not None:
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args['seed'] = p.seed
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if 'noise_sampler_seed' in possible and p.seeds is not None:
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args['noise_sampler_seed'] = p.seeds
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if 'guidance_scale' in possible and p.cfg_scale is not None and p.cfg_scale > 0:
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args['guidance_scale'] = p.cfg_scale
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if 'img_guidance_scale' in possible and hasattr(p, 'image_cfg_scale') and p.image_cfg_scale is not None and p.image_cfg_scale > 0:
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args['img_guidance_scale'] = p.image_cfg_scale
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if 'generator' in possible:
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generator = get_generator(p)
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args['generator'] = generator
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else:
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generator = None
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if 'latents' in possible and getattr(p, "init_latent", None) is not None:
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if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE:
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args['latents'] = p.init_latent
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if 'output_type' in possible:
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if not has_vae:
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kwargs['output_type'] = 'np' # only set latent if model has vae
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# model specific
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if 'Kandinsky' in model.__class__.__name__ or 'Cosmos2' in model.__class__.__name__ or 'Anima' in model.__class__.__name__ or 'OmniGen2' in model.__class__.__name__:
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kwargs['output_type'] = 'np' # only set latent if model has vae
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if 'StableCascade' in model.__class__.__name__:
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kwargs.pop("guidance_scale") # remove
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kwargs.pop("num_inference_steps") # remove
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if 'prior_num_inference_steps' in possible:
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args["prior_num_inference_steps"] = p.steps
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args["num_inference_steps"] = p.refiner_steps
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if 'prior_guidance_scale' in possible:
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args["prior_guidance_scale"] = p.cfg_scale
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if 'decoder_guidance_scale' in possible:
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args["decoder_guidance_scale"] = p.image_cfg_scale
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if 'Flex2' in model.__class__.__name__:
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if len(getattr(p, 'init_images', [])) > 0:
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args['inpaint_image'] = p.init_images[0] if isinstance(p.init_images, list) else p.init_images
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args['inpaint_mask'] = Image.new('L', args['inpaint_image'].size, int(p.denoising_strength * 255))
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args['control_image'] = args['inpaint_image'].convert('L').convert('RGB') # will be interpreted as depth
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args['control_strength'] = p.denoising_strength
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args['width'] = p.width
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args['height'] = p.height
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if 'WanVACEPipeline' in model.__class__.__name__:
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if isinstance(args['prompt'], list):
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args['prompt'] = args['prompt'][0] if len(args['prompt']) > 0 else ''
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if isinstance(args.get('negative_prompt', None), list):
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args['negative_prompt'] = args['negative_prompt'][0] if len(args['negative_prompt']) > 0 else ''
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if isinstance(args['generator'], list) and len(args['generator']) > 0:
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args['generator'] = args['generator'][0]
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# set callbacks
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if 'prior_callback_steps' in possible: # Wuerstchen / Cascade
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args['prior_callback_steps'] = 1
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elif 'callback_steps' in possible:
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args['callback_steps'] = 1
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set_callbacks_p(p)
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if 'prior_callback_on_step_end' in possible: # Wuerstchen / Cascade
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args['prior_callback_on_step_end'] = diffusers_callback
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if 'prior_callback_on_step_end_tensor_inputs' in possible:
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args['prior_callback_on_step_end_tensor_inputs'] = ['latents']
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elif 'callback_on_step_end' in possible:
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args['callback_on_step_end'] = diffusers_callback
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if 'callback_on_step_end_tensor_inputs' in possible:
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if 'HiDreamImage' in model.__class__.__name__: # uses prompt_embeds_t5 and prompt_embeds_llama3 instead
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args['callback_on_step_end_tensor_inputs'] = model._callback_tensor_inputs # pylint: disable=protected-access
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elif 'prompt_embeds' in possible and 'negative_prompt_embeds' in possible and hasattr(model, '_callback_tensor_inputs'):
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args['callback_on_step_end_tensor_inputs'] = model._callback_tensor_inputs # pylint: disable=protected-access
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else:
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args['callback_on_step_end_tensor_inputs'] = ['latents']
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elif 'callback' in possible:
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args['callback'] = diffusers_callback_legacy
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if 'image' in kwargs:
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if isinstance(kwargs['image'], list) and isinstance(kwargs['image'][0], Image.Image):
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p.init_images = kwargs['image']
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if isinstance(kwargs['image'], Image.Image):
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p.init_images = [kwargs['image']]
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if isinstance(kwargs['image'], torch.Tensor):
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p.init_images = kwargs['image']
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# handle remaining args
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for arg in kwargs:
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if arg in possible: # add kwargs
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if type(kwargs[arg]) == float or type(kwargs[arg]) == int:
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if kwargs[arg] <= -1: # skip -1 as default value
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continue
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args[arg] = kwargs[arg]
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# optional preprocess
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if hasattr(model, 'preprocess') and callable(model.preprocess):
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model.preprocess(p, args)
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# handle task specific args
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if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.MODULAR:
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task_kwargs = task_modular_kwargs(p, model)
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else:
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task_kwargs = task_specific_kwargs(p, model)
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pipe_args = getattr(p, 'task_args', {})
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model_args = getattr(model, 'task_args', {})
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task_kwargs.update(pipe_args or {})
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task_kwargs.update(model_args or {})
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if debug_enabled:
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debug_log(f'Process task args: {task_kwargs}')
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for k, v in task_kwargs.items():
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if k in possible:
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args[k] = v
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else:
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debug_log(f'Process unknown task args: {k}={v}')
|
|
|
|
# handle cross-attention args
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|
cross_attention_args = getattr(p, 'cross_attention_kwargs', {})
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|
if debug_enabled:
|
|
debug_log(f'Process cross-attention args: {cross_attention_args}')
|
|
for k, v in cross_attention_args.items():
|
|
if args.get('cross_attention_kwargs', None) is None:
|
|
args['cross_attention_kwargs'] = {}
|
|
args['cross_attention_kwargs'][k] = v
|
|
|
|
# handle missing resolution
|
|
if args.get('image', None) is not None and ('width' not in args or 'height' not in args):
|
|
if 'width' in possible and 'height' in possible:
|
|
vae_scale_factor = sd_vae.get_vae_scale_factor(model)
|
|
if isinstance(args['image'], torch.Tensor) or isinstance(args['image'], np.ndarray):
|
|
if args['image'].shape[-1] == 3: # nhwc
|
|
args['width'] = args['image'].shape[-2]
|
|
args['height'] = args['image'].shape[-3]
|
|
elif args['image'].shape[-3] == 3: # nchw
|
|
args['width'] = args['image'].shape[-1]
|
|
args['height'] = args['image'].shape[-2]
|
|
else: # assume latent
|
|
args['width'] = vae_scale_factor * args['image'].shape[-1]
|
|
args['height'] = vae_scale_factor * args['image'].shape[-2]
|
|
elif isinstance(args['image'], Image.Image):
|
|
args['width'] = args['image'].width
|
|
args['height'] = args['image'].height
|
|
elif isinstance(args['image'][0], torch.Tensor) or isinstance(args['image'][0], np.ndarray):
|
|
args['width'] = vae_scale_factor * args['image'][0].shape[-1]
|
|
args['height'] = vae_scale_factor * args['image'][0].shape[-2]
|
|
else:
|
|
args['width'] = vae_scale_factor * math.ceil(args['image'][0].width / vae_scale_factor)
|
|
args['height'] = vae_scale_factor * math.ceil(args['image'][0].height / vae_scale_factor)
|
|
if 'max_area' in possible and 'width' in args and 'height' in args and 'max_area' not in args:
|
|
args['max_area'] = args['width'] * args['height']
|
|
|
|
# handle implicit controlnet
|
|
if ('control_image' in possible) and ('control_image' not in args) and ('image' in args):
|
|
if sd_models.get_diffusers_task(model) != sd_models.DiffusersTaskType.MODULAR:
|
|
debug_log('Process: set control image')
|
|
args['control_image'] = args['image']
|
|
|
|
sd_hijack_hypertile.hypertile_set(p, hr=len(getattr(p, 'init_images', [])) > 0)
|
|
|
|
# debug info
|
|
clean = args.copy()
|
|
clean.pop('cross_attention_kwargs', None)
|
|
clean.pop('callback', None)
|
|
clean.pop('callback_steps', None)
|
|
clean.pop('callback_on_step_end', None)
|
|
clean.pop('callback_on_step_end_tensor_inputs', None)
|
|
if 'prompt' in clean and clean['prompt'] is not None:
|
|
clean['prompt'] = len(clean['prompt'])
|
|
if 'negative_prompt' in clean and clean['negative_prompt'] is not None:
|
|
clean['negative_prompt'] = len(clean['negative_prompt'])
|
|
if generator is not None:
|
|
clean['generator'] = f'{generator[0].device}:{[g.initial_seed() for g in generator]}'
|
|
clean['parser'] = prompt_attention
|
|
for k, v in clean.copy().items():
|
|
if v is None:
|
|
clean[k] = None
|
|
elif isinstance(v, torch.Tensor) or isinstance(v, np.ndarray):
|
|
clean[k] = v.shape
|
|
elif isinstance(v, list) and len(v) > 0 and (isinstance(v[0], torch.Tensor) or isinstance(v[0], np.ndarray)):
|
|
clean[k] = [x.shape for x in v]
|
|
elif not debug_enabled and k.endswith('_embeds'):
|
|
del clean[k]
|
|
clean['prompt'] = 'embeds'
|
|
task = str(sd_models.get_diffusers_task(model)).replace('DiffusersTaskType.', '')
|
|
log.info(f'{desc}: pipeline={model.__class__.__name__} task={task} batch={p.iteration + 1}/{p.n_iter}x{p.batch_size} set={clean}')
|
|
|
|
if p.hdr_clamp or p.hdr_maximize or p.hdr_brightness != 0 or p.hdr_color != 0 or p.hdr_sharpen != 0:
|
|
log.debug(f'HDR: clamp={p.hdr_clamp} maximize={p.hdr_maximize} brightness={p.hdr_brightness} color={p.hdr_color} sharpen={p.hdr_sharpen} threshold={p.hdr_threshold} boundary={p.hdr_boundary} max={p.hdr_max_boundary} center={p.hdr_max_center}')
|
|
if shared.cmd_opts.profile:
|
|
t1 = time.time()
|
|
log.debug(f'Profile: pipeline args: {t1-t0:.2f}')
|
|
if debug_enabled:
|
|
debug_log(f'Process pipeline args: {args}')
|
|
|
|
_args = {}
|
|
for k, v in args.items(): # pipeline may modify underlying args
|
|
if isinstance(v, Image.Image):
|
|
_args[k] = v.copy()
|
|
elif (isinstance(v, list) and len(v) > 0 and isinstance(v[0], Image.Image)):
|
|
_args[k] = [i.copy() for i in v]
|
|
else:
|
|
_args[k] = v
|
|
|
|
shared.state.end(argsid)
|
|
return _args
|