apply lint
This commit is contained in:
parent
d3784158fa
commit
1ee6761340
|
@ -115,7 +115,12 @@ def make_tile_mask(
|
|||
|
||||
# build gradients
|
||||
edge_t, edge_l, edge_b, edge_r = edges
|
||||
grad_x, grad_y = [int(not edge_l), 1, 1, int(not edge_r)], [int(not edge_t), 1, 1, int(not edge_b)]
|
||||
grad_x, grad_y = [int(not edge_l), 1, 1, int(not edge_r)], [
|
||||
int(not edge_t),
|
||||
1,
|
||||
1,
|
||||
int(not edge_b),
|
||||
]
|
||||
logger.debug("tile gradients: %s, %s, %s, %s", points_w, points_h, grad_x, grad_y)
|
||||
|
||||
mult_x = [np.interp(i, points_w, grad_x) for i in range(tile_w)]
|
||||
|
|
|
@ -660,8 +660,10 @@ class OnnxStableDiffusionPanoramaPipeline(DiffusionPipeline):
|
|||
region_noise_pred_uncond, region_noise_pred_text = np.split(
|
||||
region_noise_pred, 2
|
||||
)
|
||||
region_noise_pred = region_noise_pred_uncond + guidance_scale * (
|
||||
region_noise_pred_text - region_noise_pred_uncond
|
||||
region_noise_pred = (
|
||||
region_noise_pred_uncond
|
||||
+ guidance_scale
|
||||
* (region_noise_pred_text - region_noise_pred_uncond)
|
||||
)
|
||||
|
||||
# compute the previous noisy sample x_t -> x_t-1
|
||||
|
|
|
@ -502,8 +502,10 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
|
|||
region_noise_pred_uncond, region_noise_pred_text = np.split(
|
||||
region_noise_pred, 2
|
||||
)
|
||||
region_noise_pred = region_noise_pred_uncond + guidance_scale * (
|
||||
region_noise_pred_text - region_noise_pred_uncond
|
||||
region_noise_pred = (
|
||||
region_noise_pred_uncond
|
||||
+ guidance_scale
|
||||
* (region_noise_pred_text - region_noise_pred_uncond)
|
||||
)
|
||||
if guidance_rescale > 0.0:
|
||||
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
|
||||
|
|
|
@ -459,7 +459,9 @@ def slice_prompt(prompt: str, slice: int) -> str:
|
|||
return prompt
|
||||
|
||||
|
||||
Region = Tuple[int, int, int, int, float, Tuple[float, Tuple[bool, bool, bool, bool]], str]
|
||||
Region = Tuple[
|
||||
int, int, int, int, float, Tuple[float, Tuple[bool, bool, bool, bool]], str
|
||||
]
|
||||
|
||||
|
||||
def parse_region_group(group: Tuple[str, ...]) -> Region:
|
||||
|
@ -478,12 +480,15 @@ def parse_region_group(group: Tuple[str, ...]) -> Region:
|
|||
int(bottom),
|
||||
int(right),
|
||||
float(weight),
|
||||
(float(feather_radius), (
|
||||
(
|
||||
float(feather_radius),
|
||||
(
|
||||
"T" in feather_edges,
|
||||
"L" in feather_edges,
|
||||
"B" in feather_edges,
|
||||
"R" in feather_edges,
|
||||
)),
|
||||
),
|
||||
),
|
||||
prompt,
|
||||
)
|
||||
|
||||
|
|
|
@ -8,7 +8,7 @@ class NetworkModel:
|
|||
tokens: List[str]
|
||||
type: NetworkType
|
||||
|
||||
def __init__(self, name: str, type: NetworkType, tokens = None) -> None:
|
||||
def __init__(self, name: str, type: NetworkType, tokens=None) -> None:
|
||||
self.name = name
|
||||
self.tokens = tokens or []
|
||||
self.type = type
|
||||
|
|
|
@ -213,7 +213,11 @@ def load_extras(server: ServerContext):
|
|||
labels[model_name] = model["label"]
|
||||
|
||||
if "tokens" in model:
|
||||
logger.debug("collecting tokens for model %s from %s", model_name, file)
|
||||
logger.debug(
|
||||
"collecting tokens for model %s from %s",
|
||||
model_name,
|
||||
file,
|
||||
)
|
||||
extra_tokens[model_name] = model["tokens"]
|
||||
|
||||
if "inversions" in model:
|
||||
|
@ -359,7 +363,10 @@ def load_models(server: ServerContext) -> None:
|
|||
)
|
||||
logger.debug("loaded Textual Inversion models from disk: %s", inversion_models)
|
||||
network_models.extend(
|
||||
[NetworkModel(model, "inversion", tokens=extra_tokens.get(model, [])) for model in inversion_models]
|
||||
[
|
||||
NetworkModel(model, "inversion", tokens=extra_tokens.get(model, []))
|
||||
for model in inversion_models
|
||||
]
|
||||
)
|
||||
|
||||
lora_models = list_model_globs(
|
||||
|
@ -370,7 +377,12 @@ def load_models(server: ServerContext) -> None:
|
|||
base_path=path.join(server.model_path, "lora"),
|
||||
)
|
||||
logger.debug("loaded LoRA models from disk: %s", lora_models)
|
||||
network_models.extend([NetworkModel(model, "lora", tokens=extra_tokens.get(model, [])) for model in lora_models])
|
||||
network_models.extend(
|
||||
[
|
||||
NetworkModel(model, "lora", tokens=extra_tokens.get(model, []))
|
||||
for model in lora_models
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
def load_params(server: ServerContext) -> None:
|
||||
|
|
Loading…
Reference in New Issue