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Fix many precision problems

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Many users reported that image quality is different from 2.1.824. We reviewed all codes and fixed several precision problems in 2.1.846.
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lllyasviel
2023-12-16 15:55:53 -08:00
committed by GitHub
parent 3a727fd240
commit ec5dd950a2
6 changed files with 363 additions and 44 deletions
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extras/ip_adapter.py
+14 -2
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@@ -167,14 +167,26 @@ def preprocess(img, ip_adapter_path):
ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher)
pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device))
outputs = clip_vision.model(pixel_values=pixel_values, intermediate_output=-2)
if clip_vision.dtype != torch.float32:
precision_scope = torch.autocast
else:
precision_scope = lambda a, b: contextlib.nullcontext(a)
with precision_scope(ldm_patched.modules.model_management.get_autocast_device(clip_vision.load_device), torch.float32):
outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True)
ip_adapter = entry['ip_adapter']
ip_layers = entry['ip_layers']
image_proj_model = entry['image_proj_model']
ip_unconds = entry['ip_unconds']
cond = outputs[1].to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
if ip_adapter.plus:
cond = outputs.hidden_states[-2]
else:
cond = outputs.image_embeds
cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)
ldm_patched.modules.model_management.load_model_gpu(image_proj_model)
cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype)