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add pulid debug

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-11-11 09:53:05 -05:00
parent 3cd21d6b74
commit 15381eb103
2 changed files with 33 additions and 8 deletions
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installer.py
+3
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@@ -110,6 +110,9 @@ def setup_logging():
"traceback.border": "black",
"traceback.border.syntax_error": "black",
"inspect.value.border": "black",
"logging.level.info": "blue_violet",
"logging.level.debug": "purple4",
"logging.level.trace": "dark_blue",
}))
logging.basicConfig(level=logging.ERROR, format='%(asctime)s | %(name)s | %(levelname)s | %(module)s | %(message)s', handlers=[logging.NullHandler()]) # redirect default logger to null
pretty_install(console=console)
modules/pulid/pulid_sdxl.py
+30 -8
View File
@@ -4,7 +4,7 @@ import insightface
import numpy as np
import torch
import torch.nn as nn
from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
from diffusers import StableDiffusionXLPipeline
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
from huggingface_hub import hf_hub_download, snapshot_download
@@ -20,6 +20,10 @@ from insightface.app import FaceAnalysis
from eva_clip import create_model_and_transforms
from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from encoders_transformer import IDFormer, IDEncoder
from modules.errors import log
debug = log.trace if os.environ.get('SD_PULID_DEBUG', None) is not None else lambda *args, **kwargs: None
class StableDiffusionXLPuLIDPipeline:
@@ -33,14 +37,17 @@ class StableDiffusionXLPuLIDPipeline:
self.sdp = sdp
self.version = version
self.folder = 'models--ToTheBeginning--PuLID'
debug(f'PulID init: device={self.device} dtype={self.dtype} dir={self.cache_dir} offload={self.offload} sdp={self.sdp} version={self.version}')
self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
# self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
self.hack_unet_attn_layers(self.pipe.unet)
if self.version == 'v1.1':
self.id_adapter = IDFormer().to(self.device, self.dtype)
else:
self.id_adapter = IDEncoder().to(self.device, self.dtype)
debug(f'PulID load: adapter={self.id_adapter.__class__.__name__}')
self.providers = providers or ['CUDAExecutionProvider', 'CPUExecutionProvider']
self.hack_unet_attn_layers(self.pipe.unet)
debug(f'PulID load: providers={self.providers}')
# preprocessors
# face align and parsing
@@ -53,11 +60,13 @@ class StableDiffusionXLPuLIDPipeline:
device=self.device,
)
self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device)
debug(f'PulID load: facehelper={self.face_helper.__class__.__name__}')
# clip-vit backbone
eva_precision = 'fp16' if self.dtype == torch.float16 or self.dtype == torch.bfloat16 else 'fp32'
eva_model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True, precision=eva_precision, device=self.device)
self.clip_vision_model = eva_model.visual.to(dtype=self.dtype)
debug(f'PulID load: evaclip={self.clip_vision_model.__class__.__name__} precision={eva_precision}')
eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN)
eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD)
if not isinstance(eva_transform_mean, (list, tuple)):
@@ -75,9 +84,11 @@ class StableDiffusionXLPuLIDPipeline:
root=os.path.join(self.cache_dir, self.folder),
providers=self.providers,
)
debug(f'PulID load: faceanalysis={_loc}')
self.app.prepare(ctx_id=0, det_size=(640, 640))
self.handler_ante = insightface.model_zoo.get_model(os.path.join(local_dir, 'glintr100.onnx'))
self.handler_ante.prepare(ctx_id=0)
debug(f'PulID load: handler={self.handler_ante.__class__.__name__}')
self.load_pretrain()
@@ -150,6 +161,7 @@ class StableDiffusionXLPuLIDPipeline:
).to(unet.device, unet.dtype)
else:
id_adapter_attn_procs[name] = AttnProcessor()
debug(f'PulID attention: cls={IDAttnProcessor} std={AttnProcessor} len={len(id_adapter_attn_procs.keys())}')
unet.set_attn_processor(id_adapter_attn_procs)
self.id_adapter_attn_layers = nn.ModuleList(unet.attn_processors.values())
@@ -160,6 +172,7 @@ class StableDiffusionXLPuLIDPipeline:
else:
ckpt_path = hf_hub_download('guozinan/PuLID', 'pulid_v1.bin', local_dir=os.path.join(self.cache_dir, self.folder))
state_dict = torch.load(ckpt_path, map_location="cpu")
debug(f'PulID load: fn="{ckpt_path}"')
state_dict_dict = {}
for k, v in state_dict.items():
module = k.split('.')[0]
@@ -255,6 +268,7 @@ class StableDiffusionXLPuLIDPipeline:
self.clip_vision_model.to('cpu')
# return id_embedding
debug(f'PulID embedding: cond={id_embedding.shape} uncond={uncond_id_embedding.shape}')
return uncond_id_embedding, id_embedding
def set_progress_bar_config(self, bar_format: str = None, ncols: int = 80, colour: str = None):
@@ -264,9 +278,10 @@ class StableDiffusionXLPuLIDPipeline:
pulid_sampling.trange = functools.partial(trange_orig, bar_format=bar_format, ncols=ncols, colour=colour)
def sample(self, x, sigma, **extra_args):
x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
t = self.timestep(sigma)
x_ddim_space = x / (sigma[:, None, None, None] ** 2 + self.sigma_data**2) ** 0.5
cfg_scale = extra_args['cfg_scale']
debug(f'PulID sample start: step={self.step+1} x={x.shape} dtype={x.dtype} timestep={t.item()} sigma={sigma.shape} cfg={cfg_scale} args={extra_args.keys()}')
eps_positive = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['positive'])[0]
eps_negative = self.pipe.unet(x_ddim_space, t, return_dict=False, **extra_args['negative'])[0]
noise_pred = eps_negative + cfg_scale * (eps_positive - eps_negative)
@@ -274,6 +289,7 @@ class StableDiffusionXLPuLIDPipeline:
if self.callback_on_step_end is not None:
self.step += 1
self.callback_on_step_end(self.pipe, step=self.step, timestep=t, kwargs={ 'latents': latent })
debug(f'PulID sample end: step={self.step} x={latent.shape} dtype={x.dtype} min={torch.amin(latent)} max={torch.amax(latent)}')
return latent
def init_latent(self, seed, size, image, mask_image, strength, width, height): # pylint: disable=unused-argument
@@ -299,6 +315,7 @@ class StableDiffusionXLPuLIDPipeline:
return_image_latents=False,
)
latents = latents[0]
debug(f'PulID noise: op=inpaint latent={latents.shape} image={image} mask={mask_image} dtype={latents.dtype}')
else: # img2img
latents = self.pipe.prepare_latents(image,
None, # timestep (not needed)
@@ -309,10 +326,10 @@ class StableDiffusionXLPuLIDPipeline:
None, # generator
False, # add_noise
)
debug(f'PulID noise: op=img2img latent={latents.shape} image={image} dtype={latents.dtype}')
else:
latents = torch.zeros_like(noise)
debug(f'PulID noise: op=txt2img latent={latents.shape} dtype={latents.dtype}')
return latents, noise
def __call__(
@@ -333,6 +350,7 @@ class StableDiffusionXLPuLIDPipeline:
output_type: str='pil',
callback_on_step_end=None,
):
debug(f'PulID call: width={width} height={height} cfg={guidance_scale} steps={num_inference_steps} seed={seed} strength={strength} id_scale={id_scale} output={output_type}')
self.step = 0 # pylint: disable=attribute-defined-outside-init
self.callback_on_step_end = callback_on_step_end # pylint: disable=attribute-defined-outside-init
size = (1, height, width)
@@ -341,11 +359,12 @@ class StableDiffusionXLPuLIDPipeline:
if image is not None and strength > 0:
_, num_inference_steps = self.pipe.get_timesteps(num_inference_steps, strength, self.device, None) # denoising_start disabled
sigmas = sigmas[-(num_inference_steps + 1):].to(self.device) # shorten sigmas in i2i
debug(f'PulID sigmas: sigmas={sigmas.shape} dtype={sigmas.dtype}')
# latents
latent, noise = self.init_latent(seed, size, image, mask_image, strength, width, height)
noisy_latent = latent + noise * sigmas[0].to(noise)
debug(f'PulID noisy: latent={noisy_latent.shape} dtype={noisy_latent.dtype}')
(
prompt_embeds,
@@ -390,14 +409,17 @@ class StableDiffusionXLPuLIDPipeline:
latents = self.sampler(self.sample, noisy_latent, sigmas, extra_args=sampler_kwargs, disable=False, mask_args=mask_args)
# process output
latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device)
debug(f'PulID output: latent={latents.shape} dtype={latents.dtype}')
if output_type == 'latent':
images = self.pipe.image_processor.postprocess(latents, output_type='latent')
elif output_type == 'np':
images = self.pipe.image_processor.postprocess(latents, output_type='np')
else:
latents = latents.to(dtype=self.pipe.vae.dtype, device=self.device) / self.pipe.vae.config.scaling_factor
latents = latents / self.pipe.vae.config.scaling_factor
images = self.pipe.vae.decode(latents).sample
images = self.pipe.image_processor.postprocess(images, output_type='pil')
debug(f'PulID output: type={type(images)} images={images.shape if hasattr(images, "shape") else images}')
return StableDiffusionXLPipelineOutput(images)