update black, apply latest lint
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@ -612,12 +612,12 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
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attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
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if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
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new_checkpoint[
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f"down_blocks.{block_id}.downsamplers.0.conv.weight"
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] = unet_state_dict.pop(f"input_blocks.{i}.0.op.weight")
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new_checkpoint[
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f"down_blocks.{block_id}.downsamplers.0.conv.bias"
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] = unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = (
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unet_state_dict.pop(f"input_blocks.{i}.0.op.weight")
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)
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new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = (
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unet_state_dict.pop(f"input_blocks.{i}.0.op.bias")
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)
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paths = renew_resnet_paths(resnets)
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meta_path = {
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@ -705,12 +705,12 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
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index = list(output_block_list.values()).index(
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["conv.bias", "conv.weight"]
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)
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new_checkpoint[
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f"up_blocks.{block_id}.upsamplers.0.conv.weight"
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] = unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"]
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new_checkpoint[
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f"up_blocks.{block_id}.upsamplers.0.conv.bias"
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] = unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"]
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = (
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unet_state_dict[f"output_blocks.{i}.{index}.conv.weight"]
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)
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new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = (
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unet_state_dict[f"output_blocks.{i}.{index}.conv.bias"]
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)
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# Clear attentions as they have been attributed above.
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if len(attentions) == 2:
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@ -818,12 +818,12 @@ def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True):
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]
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if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
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new_checkpoint[
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f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"
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] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
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new_checkpoint[
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f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"
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] = vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
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new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = (
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vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.weight")
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)
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new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = (
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vae_state_dict.pop(f"encoder.down.{i}.downsample.conv.bias")
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)
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paths = renew_vae_resnet_paths(resnets)
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meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
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@ -871,12 +871,12 @@ def convert_ldm_vae_checkpoint(checkpoint, config, first_stage=True):
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]
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if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
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new_checkpoint[
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f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"
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] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
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new_checkpoint[
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f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"
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] = vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
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new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = (
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vae_state_dict[f"decoder.up.{block_id}.upsample.conv.weight"]
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)
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new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = (
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vae_state_dict[f"decoder.up.{block_id}.upsample.conv.bias"]
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)
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paths = renew_vae_resnet_paths(resnets)
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meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
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@ -983,9 +983,9 @@ def convert_ldm_clip_checkpoint(checkpoint):
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"text_model." + key[len("cond_stage_model.transformer.") :]
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] = checkpoint[key]
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else:
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text_model_dict[
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key[len("cond_stage_model.transformer.") :]
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] = checkpoint[key]
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text_model_dict[key[len("cond_stage_model.transformer.") :]] = (
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checkpoint[key]
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)
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text_model.load_state_dict(text_model_dict, strict=False)
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@ -1109,9 +1109,9 @@ def convert_open_clip_checkpoint(checkpoint):
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else:
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logger.debug("no projection shape found, setting to 1024")
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d_model = 1024
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text_model_dict[
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"text_model.embeddings.position_ids"
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] = text_model.text_model.embeddings.get_buffer("position_ids")
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text_model_dict["text_model.embeddings.position_ids"] = (
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text_model.text_model.embeddings.get_buffer("position_ids")
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)
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for key in keys:
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if (
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@ -465,6 +465,7 @@ class OnnxStableDiffusionLongPromptWeightingPipeline(OnnxStableDiffusionPipeline
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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"""
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if version.parse(
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version.parse(diffusers.__version__).base_version
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) >= version.parse("0.9.0"):
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@ -88,6 +88,7 @@ class OnnxStableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
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feature_extractor ([`CLIPFeatureExtractor`]):
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Model that extracts features from generated images to be used as inputs for the `safety_checker`.
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"""
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vae_encoder: OnnxRuntimeModel
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vae_decoder: OnnxRuntimeModel
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text_encoder: OnnxRuntimeModel
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@ -495,9 +495,9 @@ class BasicLayer(nn.Module):
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qk_scale=qk_scale,
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drop=drop,
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attn_drop=attn_drop,
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drop_path=drop_path[i]
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if isinstance(drop_path, list)
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else drop_path,
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drop_path=(
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drop_path[i] if isinstance(drop_path, list) else drop_path
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),
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norm_layer=norm_layer,
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)
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for i in range(depth)
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@ -244,9 +244,9 @@ def load_extras(server: ServerContext):
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inversion_name,
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model_name,
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)
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labels[
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f"inversion.{inversion_name}"
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] = inversion["label"]
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labels[f"inversion.{inversion_name}"] = (
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inversion["label"]
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)
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if "loras" in model:
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for lora in model["loras"]:
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