Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

lint fixes

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-06-18 09:56:02 +02:00
parent b60c67ecd4
commit 7c81bc2d50
5 changed files with 27 additions and 84 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/control/units/xs_pipe.py
+14 -66
View File
@@ -39,16 +39,11 @@ from diffusers.utils import (
scale_lora_layers,
unscale_lora_layers,
)
from diffusers.utils.import_utils import is_invisible_watermark_available
from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from modules.control.units.xs_model import ControlNetXSModel
if is_invisible_watermark_available():
from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
@@ -110,7 +105,7 @@ class StableDiffusionXLControlNetXSPipeline(
controlnet: ControlNetXSModel,
scheduler: KarrasDiffusionSchedulers,
force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None,
add_watermarker: Optional[bool] = None, # pylint: disable=unused-argument
):
super().__init__()
@@ -145,12 +140,6 @@ class StableDiffusionXLControlNetXSPipeline(
self.control_image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
)
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
@@ -251,7 +240,7 @@ class StableDiffusionXLControlNetXSPipeline(
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
self._lora_scale = lora_scale # pylint: disable=attribute-defined-outside-init
# dynamically adjust the LoRA scale
if self.text_encoder is not None:
@@ -286,7 +275,7 @@ class StableDiffusionXLControlNetXSPipeline(
# textual inversion: procecss multi-vector tokens if necessary
prompt_embeds_list = []
prompts = [prompt, prompt_2]
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): # pylint: disable=redefined-argument-from-local
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, tokenizer)
@@ -325,7 +314,7 @@ class StableDiffusionXLControlNetXSPipeline(
prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt # pylint: disable=no-member
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
@@ -355,7 +344,7 @@ class StableDiffusionXLControlNetXSPipeline(
uncond_tokens = [negative_prompt, negative_prompt_2]
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): # pylint: disable=redefined-argument-from-local
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
@@ -514,12 +503,12 @@ class StableDiffusionXLControlNetXSPipeline(
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
self.controlnet, torch._dynamo.eval_frame.OptimizedModule # pylint: disable=protected-access
)
if (
isinstance(self.controlnet, ControlNetXSModel)
or (is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
):
self.check_image(image, prompt, prompt_embeds)
else:
@@ -529,7 +518,7 @@ class StableDiffusionXLControlNetXSPipeline(
if (
isinstance(self.controlnet, ControlNetXSModel)
or (is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
@@ -547,7 +536,7 @@ class StableDiffusionXLControlNetXSPipeline(
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
def check_image(self, image, prompt, prompt_embeds):
def check_image(self, image, prompt, prompt_embeds): # pylint: disable=unused-argument
image_is_pil = isinstance(image, Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
@@ -567,22 +556,6 @@ class StableDiffusionXLControlNetXSPipeline(
f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
)
if image_is_pil:
image_batch_size = 1
else:
image_batch_size = len(image)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0]
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
def prepare_image(
self,
@@ -884,9 +857,6 @@ class StableDiffusionXLControlNetXSPipeline(
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
@@ -1290,7 +1260,7 @@ class StableDiffusionControlNetXSPipeline(
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, LoraLoaderMixin):
self._lora_scale = lora_scale
self._lora_scale = lora_scale # pylint: disable=attribute-defined-outside-init
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
@@ -1517,12 +1487,12 @@ class StableDiffusionControlNetXSPipeline(
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
self.controlnet, torch._dynamo.eval_frame.OptimizedModule # pylint: disable=protected-access
)
if (
isinstance(self.controlnet, ControlNetXSModel)
or (is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
):
self.check_image(image, prompt, prompt_embeds)
else:
@@ -1532,7 +1502,7 @@ class StableDiffusionControlNetXSPipeline(
if (
isinstance(self.controlnet, ControlNetXSModel)
or (is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetXSModel))
and isinstance(self.controlnet._orig_mod, ControlNetXSModel)) # pylint: disable=protected-access
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
@@ -1549,7 +1519,7 @@ class StableDiffusionControlNetXSPipeline(
if end > 1.0:
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
def check_image(self, image, prompt, prompt_embeds):
def check_image(self, image, prompt, prompt_embeds): # pylint: disable=unused-argument
image_is_pil = isinstance(image, Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
@@ -1569,22 +1539,6 @@ class StableDiffusionControlNetXSPipeline(
f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}"
)
if image_is_pil:
image_batch_size = 1
else:
image_batch_size = len(image)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif prompt_embeds is not None:
prompt_batch_size = prompt_embeds.shape[0]
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
def prepare_image(
self,
@@ -1791,9 +1745,6 @@ class StableDiffusionControlNetXSPipeline(
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
@@ -1811,9 +1762,6 @@ class StableDiffusionControlNetXSPipeline(
lora_scale=text_encoder_lora_scale,
clip_skip=clip_skip,
)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])