Merge branch 'main' into testing

fooocus_version.py

@@ -1,2 +1,2 @@
-version = '2.0.73 MRE'
+version = '2.0.76 MRE'
 full_version = 'Fooocus ' + version

modules/async_worker.py

@@ -1,18 +1,17 @@
 import threading
 
-import numpy as np
-import torch
 
 buffer = []
 outputs = []
-default_image = None
 
 
 def worker():
-    global buffer, outputs, default_image
+    global buffer, outputs
 
     import os
     import json
+    import numpy as np
+    import torch
     import time
     import shared
     import random
@@ -68,8 +67,6 @@ def progressbar(number, text):
     @torch.no_grad()
     @torch.inference_mode()
     def handler(task):
-        global default_image
-
         prompt, negative_prompt, style_selections, performance, resolution, image_number, image_seed, \
         sharpness, sampler_name, scheduler, custom_steps, custom_switch, cfg, \
         base_model_name, refiner_model_name, base_clip_skip, refiner_clip_skip, \
@@ -129,7 +126,6 @@ def handler(task):
             progressbar(0, 'Image processing ...')
             if current_tab == 'uov' and uov_method != flags.disabled and uov_input_image is not None:
                 uov_input_image = HWC3(uov_input_image)
-                default_image = uov_input_image
                 if 'vary' in uov_method:
                     if not image_is_generated_in_current_ui(uov_input_image, ui_width=width, ui_height=height):
                         uov_input_image = resize_image(uov_input_image, width=width, height=height)
@@ -207,7 +203,6 @@ def handler(task):
             if current_tab == 'inpaint' and isinstance(inpaint_input_image, dict):
                 inpaint_image = inpaint_input_image['image']
                 inpaint_mask = inpaint_input_image['mask'][:, :, 0]
-                default_image = inpaint_image
                 if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \
                         and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0):
                     if len(outpaint_selections) > 0:

modules/core.py

@@ -1,3 +1,8 @@
+from modules.patch import patch_all
+
+patch_all()
+
+
 import os
 import random
 import einops
@@ -16,11 +21,9 @@
 from comfy.model_base import SDXLRefiner
 from comfy.lora import model_lora_keys_unet, model_lora_keys_clip, load_lora
 from modules.samplers_advanced import KSamplerBasic, KSamplerWithRefiner
-from modules.patch import patch_all
 from modules.path import embeddings_path
 
 
-patch_all()
 opEmptyLatentImage = EmptyLatentImage()
 opVAEDecode = VAEDecode()
 opVAEEncode = VAEEncode()

modules/default_pipeline.py

@@ -397,7 +397,7 @@ def process_diffusion(positive_cond, negative_cond, steps, switch, width, height
             callback_function=callback
         )
 
-    decoded_latent = core.decode_vae(vae=xl_base_patched.vae, latent_image=sampled_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