feat: extract remaining attributes, do not use globals in patch
This commit is contained in:
Manuel Schmid
parent
f3222b0f27
commit
177075ff7b
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@ -1,5 +1,6 @@
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import threading
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import os
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from modules.patch import PatchSettings, patch_settings
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class AsyncTask:
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def __init__(self, args):
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@ -42,6 +43,9 @@ def worker():
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get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate
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from modules.upscaler import perform_upscale
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pid = os.getpid()
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print(f'Started worker with PID {pid}')
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try:
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async_gradio_app = shared.gradio_root
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flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}'''
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@ -227,22 +231,22 @@ def worker():
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adm_scaler_end = 0.0
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steps = 8
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modules.patch.adaptive_cfg = adaptive_cfg
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print(f'[Parameters] Adaptive CFG = {modules.patch.adaptive_cfg}')
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modules.patch.sharpness = sharpness
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print(f'[Parameters] Sharpness = {modules.patch.sharpness}')
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modules.patch.controlnet_softness = controlnet_softness
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print(f'[Parameters] ControlNet Softness = {modules.patch.controlnet_softness}')
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modules.patch.positive_adm_scale = adm_scaler_positive
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modules.patch.negative_adm_scale = adm_scaler_negative
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modules.patch.adm_scaler_end = adm_scaler_end
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print(f'[Parameters] Adaptive CFG = {adaptive_cfg}')
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print(f'[Parameters] Sharpness = {sharpness}')
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print(f'[Parameters] ControlNet Softness = {controlnet_softness}')
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print(f'[Parameters] ADM Scale = '
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f'{modules.patch.positive_adm_scale} : '
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f'{modules.patch.negative_adm_scale} : '
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f'{modules.patch.adm_scaler_end}')
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f'{adm_scaler_positive} : '
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f'{adm_scaler_negative} : '
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f'{adm_scaler_end}')
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patch_settings[pid] = PatchSettings(
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sharpness,
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adm_scaler_end,
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adm_scaler_positive,
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adm_scaler_negative,
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controlnet_softness,
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adaptive_cfg
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)
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cfg_scale = float(guidance_scale)
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print(f'[Parameters] CFG = {cfg_scale}')
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@ -815,9 +819,9 @@ def worker():
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('Sharpness', sharpness),
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('Guidance Scale', guidance_scale),
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('ADM Guidance', str((
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modules.patch.positive_adm_scale,
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modules.patch.negative_adm_scale,
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modules.patch.adm_scaler_end))),
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modules.patch.patch_settings[pid].positive_adm_scale,
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modules.patch.patch_settings[pid].negative_adm_scale,
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modules.patch.patch_settings[pid].adm_scaler_end))),
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('Base Model', base_model_name),
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('Refiner Model', refiner_model_name),
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('Refiner Switch', refiner_switch),
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@ -860,6 +864,9 @@ def worker():
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except:
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traceback.print_exc()
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task.yields.append(['finish', task.results])
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finally:
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if pid in modules.patch.patch_settings:
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del modules.patch.patch_settings[os.getpid()]
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pass
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@ -425,7 +425,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
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if refiner_swap_method == 'vae':
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modules.patch.eps_record = 'vae'
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modules.patch.patch_settings[os.getpid()].eps_record = 'vae'
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if modules.inpaint_worker.current_task is not None:
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modules.inpaint_worker.current_task.unswap()
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@ -463,7 +463,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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denoise=denoise)[switch:] * k_sigmas
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len_sigmas = len(sigmas) - 1
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noise_mean = torch.mean(modules.patch.eps_record, dim=1, keepdim=True)
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noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True)
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if modules.inpaint_worker.current_task is not None:
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modules.inpaint_worker.current_task.swap()
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@ -493,5 +493,5 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled)
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images = core.pytorch_to_numpy(decoded_latent)
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modules.patch.eps_record = None
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modules.patch.patch_settings[os.getpid()].eps_record = None
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return images
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@ -27,20 +27,27 @@ from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timeste
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from modules.patch_precision import patch_all_precision
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from modules.patch_clip import patch_all_clip
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# TODO make these parameters dynamic:
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# TODO sharpness, adm_scaler_end, positive_adm_scale, negative_adm_scale, adaptive_cfg + controlnet_softness
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sharpness = 2.0
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class PatchSettings:
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def __init__(self,
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sharpness=2.0,
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adm_scaler_end=0.3,
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positive_adm_scale=1.5,
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negative_adm_scale=0.8,
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controlnet_softness=0.25,
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adaptive_cfg=7.0):
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self.sharpness = sharpness
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self.adm_scaler_end = adm_scaler_end
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self.positive_adm_scale = positive_adm_scale
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self.negative_adm_scale = negative_adm_scale
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self.controlnet_softness = controlnet_softness
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self.adaptive_cfg = adaptive_cfg
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self.global_diffusion_progress = 0
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self.eps_record = None
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adm_scaler_end = 0.3
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positive_adm_scale = 1.5
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negative_adm_scale = 0.8
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controlnet_softness = 0.25
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patch_settings = {}
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adaptive_cfg = 7.0
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global_diffusion_progress = 0
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eps_record = None
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def calculate_weight_patched(self, patches, weight, key):
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for p in patches:
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@ -203,14 +210,12 @@ class BrownianTreeNoiseSamplerPatched:
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def compute_cfg(uncond, cond, cfg_scale, t):
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global adaptive_cfg
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mimic_cfg = float(adaptive_cfg)
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mimic_cfg = float(patch_settings[os.getpid()].adaptive_cfg)
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real_cfg = float(cfg_scale)
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real_eps = uncond + real_cfg * (cond - uncond)
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if cfg_scale > adaptive_cfg:
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if cfg_scale > patch_settings[os.getpid()].adaptive_cfg:
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mimicked_eps = uncond + mimic_cfg * (cond - uncond)
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return real_eps * t + mimicked_eps * (1 - t)
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else:
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@ -218,13 +223,11 @@ def compute_cfg(uncond, cond, cfg_scale, t):
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def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None):
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global eps_record
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if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False):
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final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0]
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if eps_record is not None:
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eps_record = ((x - final_x0) / timestep).cpu()
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if patch_settings[os.getpid()].eps_record is not None:
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patch_settings[os.getpid()].eps_record = ((x - final_x0) / timestep).cpu()
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return final_x0
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@ -233,16 +236,16 @@ def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, mode
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positive_eps = x - positive_x0
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negative_eps = x - negative_x0
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alpha = 0.001 * sharpness * global_diffusion_progress
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alpha = 0.001 * patch_settings[os.getpid()].sharpness * patch_settings[os.getpid()].global_diffusion_progress
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positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
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positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha)
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final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted,
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cfg_scale=cond_scale, t=global_diffusion_progress)
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cfg_scale=cond_scale, t=patch_settings[os.getpid()].global_diffusion_progress)
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if eps_record is not None:
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eps_record = (final_eps / timestep).cpu()
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if patch_settings[os.getpid()].eps_record is not None:
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patch_settings[os.getpid()].eps_record = (final_eps / timestep).cpu()
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return x - final_eps
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@ -257,8 +260,6 @@ def round_to_64(x):
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def sdxl_encode_adm_patched(self, **kwargs):
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global positive_adm_scale, negative_adm_scale
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clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor)
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width = kwargs.get("width", 1024)
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height = kwargs.get("height", 1024)
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@ -266,11 +267,11 @@ def sdxl_encode_adm_patched(self, **kwargs):
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target_height = height
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if kwargs.get("prompt_type", "") == "negative":
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width = float(width) * negative_adm_scale
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height = float(height) * negative_adm_scale
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width = float(width) * patch_settings[os.getpid()].negative_adm_scale
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height = float(height) * patch_settings[os.getpid()].negative_adm_scale
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elif kwargs.get("prompt_type", "") == "positive":
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width = float(width) * positive_adm_scale
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height = float(height) * positive_adm_scale
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width = float(width) * patch_settings[os.getpid()].positive_adm_scale
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height = float(height) * patch_settings[os.getpid()].positive_adm_scale
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def embedder(number_list):
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h = self.embedder(torch.tensor(number_list, dtype=torch.float32))
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@ -324,7 +325,7 @@ def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale,
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def timed_adm(y, timesteps):
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if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632:
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y_mask = (timesteps > 999.0 * (1.0 - float(adm_scaler_end))).to(y)[..., None]
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y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None]
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y_with_adm = y[..., :2816].clone()
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y_without_adm = y[..., 2816:].clone()
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return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask)
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@ -359,19 +360,17 @@ def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs):
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h = self.middle_block(h, emb, context)
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outs.append(self.middle_block_out(h, emb, context))
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if controlnet_softness > 0:
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if patch_settings[os.getpid()].controlnet_softness > 0:
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for i in range(10):
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k = 1.0 - float(i) / 9.0
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outs[i] = outs[i] * (1.0 - controlnet_softness * k)
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outs[i] = outs[i] * (1.0 - patch_settings[os.getpid()].controlnet_softness * k)
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return outs
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def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
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global global_diffusion_progress
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self.current_step = 1.0 - timesteps.to(x) / 999.0
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global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
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patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0])
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y = timed_adm(y, timesteps)
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@ -485,7 +484,7 @@ def patch_all():
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if ldm_patched.modules.model_management.directml_enabled:
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ldm_patched.modules.model_management.lowvram_available = True
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ldm_patched.modules.model_management.OOM_EXCEPTION = Exception
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patch_all_precision()
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patch_all_clip()
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