ruff manual fixes (550256db) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/LDSR/sd_hijack_autoencoder.py

+5 −5

Original line number	Diff line number	Diff line
		@@ -24,7 +24,7 @@ class VQModel(pl.LightningModule):
		n_embed,
		embed_dim,
		ckpt_path=None,
		ignore_keys=[],
		ignore_keys=None,
		image_key="image",
		colorize_nlabels=None,
		monitor=None,
		@@ -62,7 +62,7 @@ class VQModel(pl.LightningModule):
		print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")

		if ckpt_path is not None:
		self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
		self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
		self.scheduler_config = scheduler_config
		self.lr_g_factor = lr_g_factor

		@@ -81,11 +81,11 @@ class VQModel(pl.LightningModule):
		if context is not None:
		print(f"{context}: Restored training weights")

		def init_from_ckpt(self, path, ignore_keys=list()):
		def init_from_ckpt(self, path, ignore_keys=None):
		sd = torch.load(path, map_location="cpu")["state_dict"]
		keys = list(sd.keys())
		for k in keys:
		for ik in ignore_keys:
		for ik in ignore_keys or []:
		if k.startswith(ik):
		print("Deleting key {} from state_dict.".format(k))
		del sd[k]
		@@ -270,7 +270,7 @@ class VQModel(pl.LightningModule):

		class VQModelInterface(VQModel):
		def __init__(self, embed_dim, args, *kwargs):
		super().__init__(embed_dim=embed_dim, args, *kwargs)
		super().__init__(args, embed_dim=embed_dim, *kwargs)
		self.embed_dim = embed_dim

		def encode(self, x):

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

+7 −7

Original line number	Diff line number	Diff line
		@@ -48,7 +48,7 @@ class DDPMV1(pl.LightningModule):
		beta_schedule="linear",
		loss_type="l2",
		ckpt_path=None,
		ignore_keys=[],
		ignore_keys=None,
		load_only_unet=False,
		monitor="val/loss",
		use_ema=True,
		@@ -100,7 +100,7 @@ class DDPMV1(pl.LightningModule):
		if monitor is not None:
		self.monitor = monitor
		if ckpt_path is not None:
		self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
		self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)

		self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
		linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
		@@ -182,13 +182,13 @@ class DDPMV1(pl.LightningModule):
		if context is not None:
		print(f"{context}: Restored training weights")

		def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
		def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
		sd = torch.load(path, map_location="cpu")
		if "state_dict" in list(sd.keys()):
		sd = sd["state_dict"]
		keys = list(sd.keys())
		for k in keys:
		for ik in ignore_keys:
		for ik in ignore_keys or []:
		if k.startswith(ik):
		print("Deleting key {} from state_dict.".format(k))
		del sd[k]
		@@ -444,7 +444,7 @@ class LatentDiffusionV1(DDPMV1):
		conditioning_key = None
		ckpt_path = kwargs.pop("ckpt_path", None)
		ignore_keys = kwargs.pop("ignore_keys", [])
		super().__init__(conditioning_key=conditioning_key, args, *kwargs)
		super().__init__(args, conditioning_key=conditioning_key, *kwargs)
		self.concat_mode = concat_mode
		self.cond_stage_trainable = cond_stage_trainable
		self.cond_stage_key = cond_stage_key
		@@ -1418,10 +1418,10 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
		# TODO: move all layout-specific hacks to this class
		def __init__(self, cond_stage_key, args, *kwargs):
		assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
		super().__init__(cond_stage_key=cond_stage_key, args, *kwargs)
		super().__init__(args, cond_stage_key=cond_stage_key, *kwargs)

		def log_images(self, batch, N=8, args, *kwargs):
		logs = super().log_images(batch=batch, N=N, args, *kwargs)
		logs = super().log_images(args, batch=batch, N=N, *kwargs)

		key = 'train' if self.training else 'validation'
		dset = self.trainer.datamodule.datasets[key]

extensions-builtin/SwinIR/swinir_model_arch.py

+5 −1

Original line number	Diff line number	Diff line
		@@ -644,13 +644,17 @@ class SwinIR(nn.Module):
		"""

		def __init__(self, img_size=64, patch_size=1, in_chans=3,
		embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
		embed_dim=96, depths=None, num_heads=None,
		window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
		drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
		norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
		use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
		**kwargs):
		super(SwinIR, self).__init__()

		depths = depths or [6, 6, 6, 6]
		num_heads = num_heads or [6, 6, 6, 6]

		num_in_ch = in_chans
		num_out_ch = in_chans
		num_feat = 64

extensions-builtin/SwinIR/swinir_model_arch_v2.py

+9 −2

Original line number	Diff line number	Diff line
		@@ -74,9 +74,12 @@ class WindowAttention(nn.Module):
		"""

		def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
		pretrained_window_size=[0, 0]):
		pretrained_window_size=None):

		super().__init__()

		pretrained_window_size = pretrained_window_size or [0, 0]

		self.dim = dim
		self.window_size = window_size # Wh, Ww
		self.pretrained_window_size = pretrained_window_size
		@@ -698,13 +701,17 @@ class Swin2SR(nn.Module):
		"""

		def __init__(self, img_size=64, patch_size=1, in_chans=3,
		embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
		embed_dim=96, depths=None, num_heads=None,
		window_size=7, mlp_ratio=4., qkv_bias=True,
		drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
		norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
		use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
		**kwargs):
		super(Swin2SR, self).__init__()

		depths = depths or [6, 6, 6, 6]
		num_heads = num_heads or [6, 6, 6, 6]

		num_in_ch = in_chans
		num_out_ch = in_chans
		num_feat = 64

modules/api/api.py

+12 −6

Original line number	Diff line number	Diff line
		@@ -34,14 +34,16 @@ import piexif.helper
		def upscaler_to_index(name: str):
		try:
		return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
		except Exception:
		raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}")
		except Exception as e:
		raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in shared.sd_upscalers])}") from e


		def script_name_to_index(name, scripts):
		try:
		return [script.title().lower() for script in scripts].index(name.lower())
		except Exception:
		raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
		except Exception as e:
		raise HTTPException(status_code=422, detail=f"Script '{name}' not found") from e


		def validate_sampler_name(name):
		config = sd_samplers.all_samplers_map.get(name, None)
		@@ -50,20 +52,23 @@ def validate_sampler_name(name):

		return name


		def setUpscalers(req: dict):
		reqDict = vars(req)
		reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
		reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
		return reqDict


		def decode_base64_to_image(encoding):
		if encoding.startswith("data:image/"):
		encoding = encoding.split(";")[1].split(",")[1]
		try:
		image = Image.open(BytesIO(base64.b64decode(encoding)))
		return image
		except Exception:
		raise HTTPException(status_code=500, detail="Invalid encoded image")
		except Exception as e:
		raise HTTPException(status_code=500, detail="Invalid encoded image") from e


		def encode_pil_to_base64(image):
		with io.BytesIO() as output_bytes:
		@@ -94,6 +99,7 @@ def encode_pil_to_base64(image):

		return base64.b64encode(bytes_data)


		def api_middleware(app: FastAPI):
		rich_available = True
		try:

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