better sigmas for sd1.5 as refiner
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lllyasviel
GitHub
parent
26fe88d8a6
commit
3b9266e848
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@ -1 +1 @@
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version = '2.1.53'
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version = '2.1.54'
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@ -204,17 +204,19 @@ def get_previewer(model):
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def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_fooocus_2m_sde_inpaint_seamless',
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scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None,
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force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1,
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previewer_start=None, previewer_end=None, sigmas=None):
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previewer_start=None, previewer_end=None, sigmas=None, noise=None):
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if sigmas is not None:
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sigmas = sigmas.clone().to(fcbh.model_management.get_torch_device())
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latent_image = latent["samples"]
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_inds = latent["batch_index"] if "batch_index" in latent else None
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noise = fcbh.sample.prepare_noise(latent_image, seed, batch_inds)
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if noise is None:
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if disable_noise:
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noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
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else:
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batch_inds = latent["batch_index"] if "batch_index" in latent else None
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noise = fcbh.sample.prepare_noise(latent_image, seed, batch_inds)
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noise_mask = None
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if "noise_mask" in latent:
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@ -5,6 +5,7 @@ import modules.path
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import fcbh.model_management
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import fcbh.latent_formats
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import modules.inpaint_worker
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import modules.sample_hijack as sample_hijack
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from fcbh.model_base import SDXL, SDXLRefiner
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from modules.expansion import FooocusExpansion
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@ -327,6 +328,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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else:
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empty_latent = latent
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decoded_latent = None
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if refiner_swap_method == 'joint':
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sampled_latent = core.ksampler(
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model=final_unet,
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@ -346,8 +349,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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previewer_end=steps,
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)
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decoded_latent = core.decode_vae(vae=final_vae, latent_image=sampled_latent, tiled=tiled)
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images = core.pytorch_to_numpy(decoded_latent)
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return images
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if refiner_swap_method == 'upscale':
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target_model = final_refiner_unet
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@ -374,8 +375,6 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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if target_model is None:
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target_model = final_vae
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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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return images
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if refiner_swap_method == 'separate':
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sampled_latent = core.ksampler(
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@ -420,23 +419,10 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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if target_model is None:
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target_model = final_vae
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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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return images
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if refiner_swap_method == 'vae':
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sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise)
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sigmas_a = sigmas[:switch]
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sigmas_b = sigmas[switch:]
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if final_refiner_unet is not None:
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k1 = final_refiner_unet.model.latent_format.scale_factor
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k2 = final_unet.model.latent_format.scale_factor
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k = float(k1) / float(k2)
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sigmas_b = sigmas_b * k
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sigmas = torch.cat([sigmas_a, sigmas_b], dim=0)
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sampled_latent = core.ksampler(
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sample_hijack.history_record = []
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core.ksampler(
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model=final_unet,
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positive=positive_cond,
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negative=negative_cond,
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@ -449,8 +435,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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sampler_name=sampler_name,
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scheduler=scheduler_name,
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previewer_start=0,
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previewer_end=steps,
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sigmas=sigmas
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previewer_end=steps
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)
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print('Fooocus VAE-based swap.')
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@ -459,6 +444,28 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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target_model = final_unet
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print('Use base model to refine itself - this may because of developer mode.')
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sigmas = None
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len_sigmas = steps - switch
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if final_refiner_unet is not None:
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sigmas = calculate_sigmas(sampler=sampler_name,
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scheduler=scheduler_name,
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model=final_refiner_unet.model,
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steps=steps,
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denoise=denoise)[switch:]
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k1 = final_refiner_unet.model.latent_format.scale_factor
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k2 = final_unet.model.latent_format.scale_factor
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k = float(k1) / float(k2)
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sigmas = sigmas * k
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len_sigmas = len(sigmas) - 1
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last_step, last_clean_latent, last_noisy_latent = sample_hijack.history_record[-1]
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last_clean_latent = final_unet.model.process_latent_out(last_clean_latent.cpu().to(torch.float32))
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last_noisy_latent = final_unet.model.process_latent_out(last_noisy_latent.cpu().to(torch.float32))
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last_noise = last_noisy_latent - last_clean_latent
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last_noise = last_noise / last_noise.std()
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sampled_latent = {'samples': last_clean_latent}
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sampled_latent = vae_parse(sampled_latent)
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if modules.inpaint_worker.current_task is not None:
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@ -469,8 +476,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=final_clip),
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negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=final_clip),
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latent=sampled_latent,
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steps=steps, start_step=switch, last_step=steps, disable_noise=False, force_full_denoise=True,
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seed=image_seed,
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steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True,
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seed=image_seed+1, # Avoid artifacts
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denoise=denoise,
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callback_function=callback,
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cfg=cfg_scale,
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@ -478,7 +485,8 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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scheduler=scheduler_name,
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previewer_start=switch,
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previewer_end=steps,
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sigmas=sigmas
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sigmas=sigmas,
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noise=last_noise
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)
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if modules.inpaint_worker.current_task is not None:
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@ -488,5 +496,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
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if target_model is None:
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target_model = final_vae
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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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return images
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images = core.pytorch_to_numpy(decoded_latent)
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sample_hijack.history_record = None
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return images
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@ -11,6 +11,7 @@ from fcbh.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_st
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current_refiner = None
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refiner_switch_step = -1
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history_record = None
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@torch.no_grad()
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@ -127,6 +128,9 @@ def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas
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return
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def callback_wrap(step, x0, x, total_steps):
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global history_record
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if isinstance(history_record, list):
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history_record.append((step, x0, x))
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if step == refiner_switch_step and current_refiner is not None:
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refiner_switch()
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if callback is not None:
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