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chore: ruff format

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This commit is contained in:
psychedelicious
2023-11-11 10:55:06 +11:00
parent 513fceac82
commit 6494e8e551
18 changed files with 80 additions and 54 deletions
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14
invokeai/backend/install/model_install_backend.py
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@ -253,13 +253,13 @@ class ModelInstall(object):
# folders style or similar
elif path.is_dir() and any(
(path / x).exists()
for x in {
"config.json",
"model_index.json",
"learned_embeds.bin",
"pytorch_lora_weights.bin",
"pytorch_lora_weights.safetensors",
}
for x in {
"config.json",
"model_index.json",
"learned_embeds.bin",
"pytorch_lora_weights.bin",
"pytorch_lora_weights.safetensors",
}
):
models_installed.update({str(model_path_id_or_url): self._install_path(path)})

4
invokeai/backend/ip_adapter/attention_processor.py
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@ -130,7 +130,9 @@ class IPAttnProcessor2_0(torch.nn.Module):
assert ip_adapter_image_prompt_embeds is not None
assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True):
for ipa_embed, ipa_weights, scale in zip(
ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True
):
# The batch dimensions should match.
assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
# The token_len dimensions should match.

2
invokeai/backend/model_management/model_cache.py
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@ -66,11 +66,13 @@ class CacheStats(object):
class ModelLocker(object):
"Forward declaration"
pass
class ModelCache(object):
"Forward declaration"
pass

14
invokeai/backend/model_management/model_search.py
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@ -70,13 +70,13 @@ class ModelSearch(ABC):
continue
if any(
(path / x).exists()
for x in {
"config.json",
"model_index.json",
"learned_embeds.bin",
"pytorch_lora_weights.bin",
"image_encoder.txt",
}
for x in {
"config.json",
"model_index.json",
"learned_embeds.bin",
"pytorch_lora_weights.bin",
"image_encoder.txt",
}
):
try:
self.on_model_found(path)

1
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -193,6 +193,7 @@ class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
attention_map_saver (`AttentionMapSaver`): Object containing attention maps that can be displayed to the user
after generation completes. Optional.
"""
attention_map_saver: Optional[AttentionMapSaver]

2
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@ -433,7 +433,7 @@ def inject_attention_function(unet, context: Context):
module.identifier = identifier
try:
module.set_attention_slice_wrangler(attention_slice_wrangler)
module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier)) # noqa: B023
module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier)) # noqa: B023
except AttributeError as e:
if is_attribute_error_about(e, "set_attention_slice_wrangler"):
print(f"TODO: implement set_attention_slice_wrangler for {type(module)}") # TODO

4
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -642,7 +642,9 @@ class InvokeAIDiffuserComponent:
deltas = None
uncond_latents = None
weighted_cond_list = c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
weighted_cond_list = (
c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
)
# below is fugly omg
conditionings = [uc] + [c for c, weight in weighted_cond_list]

8
invokeai/backend/util/hotfixes.py
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@ -732,7 +732,9 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
controlnet_down_block_res_samples = ()
for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks, strict=True):
for down_block_res_sample, controlnet_block in zip(
down_block_res_samples, self.controlnet_down_blocks, strict=True
):
down_block_res_sample = controlnet_block(down_block_res_sample)
controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
@ -745,7 +747,9 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device) # 0.1 to 1.0
scales = scales * conditioning_scale
down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales, strict=True)]
down_block_res_samples = [
sample * scale for sample, scale in zip(down_block_res_samples, scales, strict=True)
]
mid_block_res_sample = mid_block_res_sample * scales[-1] # last one
else:
down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]

6
invokeai/backend/util/util.py
View File

@ -229,7 +229,11 @@ def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1).to(device)
def tile_grads(slice1, slice2):
return gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
return (
gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
.repeat_interleave(d[0], 0)
.repeat_interleave(d[1], 1)
)
def dot(grad, shift):
return (