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[Fooocus 2.0.50] Variation/Upscale (Midjourney Toolbar) (#389)

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lllyasviel
2023-09-16 03:29:41 -07:00
committed by GitHub
parent 58c29aed00
commit 8ef31d33af
15 changed files with 446 additions and 100 deletions
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modules/default_pipeline.py
+23 -5
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@@ -3,7 +3,6 @@ import os
import torch
import modules.path
import modules.virtual_memory as virtual_memory
import comfy.model_management as model_management
from comfy.model_base import SDXL, SDXLRefiner
from modules.patch import cfg_patched
@@ -20,6 +19,8 @@ xl_base_patched: core.StableDiffusionModel = None
xl_base_patched_hash = ''
@torch.no_grad()
@torch.inference_mode()
def refresh_base_model(name):
global xl_base, xl_base_hash, xl_base_patched, xl_base_patched_hash
@@ -51,6 +52,8 @@ def refresh_base_model(name):
return
@torch.no_grad()
@torch.inference_mode()
def refresh_refiner_model(name):
global xl_refiner, xl_refiner_hash
@@ -86,6 +89,8 @@ def refresh_refiner_model(name):
return
@torch.no_grad()
@torch.inference_mode()
def refresh_loras(loras):
global xl_base, xl_base_patched, xl_base_patched_hash
if xl_base_patched_hash == str(loras):
@@ -106,6 +111,7 @@ def refresh_loras(loras):
@torch.no_grad()
@torch.inference_mode()
def clip_encode_single(clip, text, verbose=False):
cached = clip.fcs_cond_cache.get(text, None)
if cached is not None:
@@ -121,6 +127,7 @@ def clip_encode_single(clip, text, verbose=False):
@torch.no_grad()
@torch.inference_mode()
def clip_encode(sd, texts, pool_top_k=1):
if sd is None:
return None
@@ -145,6 +152,7 @@ def clip_encode(sd, texts, pool_top_k=1):
@torch.no_grad()
@torch.inference_mode()
def clear_sd_cond_cache(sd):
if sd is None:
return None
@@ -155,11 +163,14 @@ def clear_sd_cond_cache(sd):
@torch.no_grad()
@torch.inference_mode()
def clear_all_caches():
clear_sd_cond_cache(xl_base_patched)
clear_sd_cond_cache(xl_refiner)
@torch.no_grad()
@torch.inference_mode()
def refresh_everything(refiner_model_name, base_model_name, loras):
refresh_refiner_model(refiner_model_name)
if xl_refiner is not None:
@@ -184,6 +195,7 @@ expansion = FooocusExpansion()
@torch.no_grad()
@torch.inference_mode()
def patch_all_models():
assert xl_base is not None
assert xl_base_patched is not None
@@ -198,14 +210,18 @@ def patch_all_models():
@torch.no_grad()
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback):
@torch.inference_mode()
def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, latent=None, denoise=1.0, tiled=False):
patch_all_models()
if xl_refiner is not None:
virtual_memory.try_move_to_virtual_memory(xl_refiner.unet.model)
virtual_memory.load_from_virtual_memory(xl_base.unet.model)
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
if latent is None:
empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=1)
else:
empty_latent = latent
if xl_refiner is not None:
sampled_latent = core.ksampler_with_refiner(
@@ -219,6 +235,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
latent=empty_latent,
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
seed=image_seed,
denoise=denoise,
callback_function=callback
)
else:
@@ -229,9 +246,10 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
latent=empty_latent,
steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True,
seed=image_seed,
denoise=denoise,
callback_function=callback
)
decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent)
images = core.image_to_numpy(decoded_latent)
decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_latent, tiled=tiled)
images = core.pytorch_to_numpy(decoded_latent)
return images