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minor fix (#443)

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* minor fix

* minor fix
This commit is contained in:
lllyasviel
2023-09-19 18:04:36 -07:00
committed by GitHub
parent 969a54e85b
commit b6752c74e3
2 changed files with 7 additions and 4 deletions
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fooocus_version.py
+1 -1
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@@ -1 +1 @@
version = '2.0.68' version = '2.0.70'
modules/async_worker.py
+6 -3
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@@ -55,6 +55,7 @@ def worker():
outpaint_selections = [o.lower() for o in outpaint_selections] outpaint_selections = [o.lower() for o in outpaint_selections]
loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)] loras = [(l1, w1), (l2, w2), (l3, w3), (l4, w4), (l5, w5)]
loras_user_raw_input = copy.deepcopy(loras)
raw_style_selections = copy.deepcopy(style_selections) raw_style_selections = copy.deepcopy(style_selections)
@@ -207,8 +208,6 @@ def worker():
inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None]) inpaint_mask = core.numpy_to_pytorch(inpaint_worker.current_task.mask_ready[None])
inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8)) inpaint_mask = torch.nn.functional.avg_pool2d(inpaint_mask, (8, 8))
inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear') inpaint_mask = torch.nn.functional.interpolate(inpaint_mask, (H, W), mode='bilinear')
width = W * 8
height = H * 8
inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask) inpaint_worker.current_task.load_latent(latent=inpaint_latent, mask=inpaint_mask)
progressbar(0, 'VAE inpaint encoding ...') progressbar(0, 'VAE inpaint encoding ...')
@@ -223,6 +222,10 @@ def worker():
inpaint_mask = vae_dict['noise_mask'] inpaint_mask = vae_dict['noise_mask']
inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask, model_path=inpaint_head_model_path) inpaint_worker.current_task.load_inpaint_guidance(latent=inpaint_latent, mask=inpaint_mask, model_path=inpaint_head_model_path)
B, C, H, W = inpaint_latent.shape
height, width = inpaint_worker.current_task.image_raw.shape[:2]
print(f'Final resolution is {str((height, width))}, latent is {str((H * 8, W * 8))}.')
progressbar(1, 'Initializing ...') progressbar(1, 'Initializing ...')
raw_prompt = prompt raw_prompt = prompt
@@ -363,7 +366,7 @@ def worker():
('Refiner Model', refiner_model_name), ('Refiner Model', refiner_model_name),
('Seed', task['task_seed']) ('Seed', task['task_seed'])
] ]
for n, w in loras: for n, w in loras_user_raw_input:
if n != 'None': if n != 'None':
d.append((f'LoRA [{n}] weight', w)) d.append((f'LoRA [{n}] weight', w))
log(x, d, single_line_number=3) log(x, d, single_line_number=3)