mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Run ruff, setup initial text to image node
This commit is contained in:
Brandon Rising
Brandon
parent
436f18ff55
commit
1bd90e0fd4
@ -1,8 +1,4 @@
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import torch
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from einops import repeat
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from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
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from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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@ -10,9 +6,8 @@ from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
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from invokeai.app.invocations.model import CLIPField, T5EncoderField
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from invokeai.app.invocations.primitives import ConditioningOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
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from invokeai.backend.util.devices import TorchDevice
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from invokeai.backend.flux.modules.conditioner import HFEncoder
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData, FLUXConditioningInfo
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@invocation(
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@ -1,12 +1,6 @@
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from typing import Literal
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import accelerate
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import torch
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from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
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from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
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from einops import rearrange, repeat
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from PIL import Image
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from safetensors.torch import load_file
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from transformers.models.auto import AutoModelForTextEncoding
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.fields import (
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@ -20,23 +14,12 @@ from invokeai.app.invocations.fields import (
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from invokeai.app.invocations.model import TransformerField, VAEField
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from invokeai.app.invocations.primitives import ImageOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.quantization.bnb_nf4 import quantize_model_nf4
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from invokeai.backend.quantization.fast_quantized_diffusion_model import FastQuantizedDiffusersModel
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from invokeai.backend.quantization.fast_quantized_transformers_model import FastQuantizedTransformersModel
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from invokeai.backend.flux.model import Flux
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from invokeai.backend.flux.modules.autoencoder import AutoEncoder
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from invokeai.backend.flux.sampling import denoise, get_noise, get_schedule, unpack
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import FLUXConditioningInfo
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from invokeai.backend.util.devices import TorchDevice
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TFluxModelKeys = Literal["flux-schnell"]
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FLUX_MODELS: dict[TFluxModelKeys, str] = {"flux-schnell": "black-forest-labs/FLUX.1-schnell"}
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class QuantizedFluxTransformer2DModel(FastQuantizedDiffusersModel):
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base_class = FluxTransformer2DModel
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class QuantizedModelForTextEncoding(FastQuantizedTransformersModel):
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auto_class = AutoModelForTextEncoding
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@invocation(
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"flux_text_to_image",
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@ -78,7 +61,7 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
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assert isinstance(flux_conditioning, FLUXConditioningInfo)
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latents = self._run_diffusion(context, flux_conditioning.clip_embeds, flux_conditioning.t5_embeds)
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image = self._run_vae_decoding(context, latents)
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image = self._run_vae_decoding(context, flux_ae_path, latents)
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image_dto = context.images.save(image=image)
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return ImageOutput.build(image_dto)
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@ -89,14 +72,40 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
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t5_embeddings: torch.Tensor,
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):
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transformer_info = context.models.load(self.transformer.transformer)
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inference_dtype = TorchDevice.choose_torch_dtype()
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# Prepare input noise.
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# TODO(ryand): Does the seed behave the same on different devices? Should we re-implement this to always use a
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# CPU RNG?
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x = get_noise(
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num_samples=1,
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height=self.height,
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width=self.width,
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device=TorchDevice.choose_torch_device(),
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dtype=inference_dtype,
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seed=self.seed,
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)
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img, img_ids = self._prepare_latent_img_patches(x)
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# HACK(ryand): Find a better way to determine if this is a schnell model or not.
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is_schnell = "shnell" in transformer_info.config.path if transformer_info.config else ""
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timesteps = get_schedule(
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num_steps=self.num_steps,
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image_seq_len=img.shape[1],
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shift=not is_schnell,
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)
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bs, t5_seq_len, _ = t5_embeddings.shape
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txt_ids = torch.zeros(bs, t5_seq_len, 3, dtype=inference_dtype, device=TorchDevice.choose_torch_device())
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# HACK(ryand): Manually empty the cache. Currently we don't check the size of the model before loading it from
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# disk. Since the transformer model is large (24GB), there's a good chance that it will OOM on 32GB RAM systems
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# if the cache is not empty.
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# context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
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context.models._services.model_manager.load.ram_cache.make_room(24 * 2**30)
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with transformer_info as transformer:
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assert isinstance(transformer, FluxTransformer2DModel)
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assert isinstance(transformer, Flux)
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x = denoise(
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model=transformer,
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@ -144,21 +153,13 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
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) -> Image.Image:
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vae_info = context.models.load(self.vae.vae)
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with vae_info as vae:
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assert isinstance(vae, AutoencoderKL)
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assert isinstance(vae, AutoEncoder)
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# TODO(ryand): Test that this works with both float16 and bfloat16.
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with torch.autocast(device_type=latents.device.type, dtype=TorchDevice.choose_torch_dtype()):
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img = vae.decode(latents)
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img.clamp(-1, 1)
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img = rearrange(img[0], "c h w -> h w c")
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img_pil = Image.fromarray((127.5 * (img + 1.0)).byte().cpu().numpy())
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latents = flux_pipeline_with_vae._unpack_latents(
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latents, self.height, self.width, flux_pipeline_with_vae.vae_scale_factor
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)
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latents = (
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latents / flux_pipeline_with_vae.vae.config.scaling_factor
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) + flux_pipeline_with_vae.vae.config.shift_factor
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latents = latents.to(dtype=vae.dtype)
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image = flux_pipeline_with_vae.vae.decode(latents, return_dict=False)[0]
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image = flux_pipeline_with_vae.image_processor.postprocess(image, output_type="pil")[0]
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assert isinstance(image, Image.Image)
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return image
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return img_pil
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@ -1,6 +1,6 @@
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import copy
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from time import sleep
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from typing import List, Optional, Literal, Dict
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from typing import Dict, List, Literal, Optional
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from pydantic import BaseModel, Field
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@ -12,10 +12,10 @@ from invokeai.app.invocations.baseinvocation import (
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invocation_output,
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)
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
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from invokeai.app.services.model_records import ModelRecordChanges
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.shared.models import FreeUConfig
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from invokeai.app.services.model_records import ModelRecordChanges
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from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType, ModelFormat
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from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
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class ModelIdentifierField(BaseModel):
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@ -132,31 +132,22 @@ class ModelIdentifierInvocation(BaseInvocation):
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return ModelIdentifierOutput(model=self.model)
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T5_ENCODER_OPTIONS = Literal["base", "16b_quantized", "8b_quantized"]
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T5_ENCODER_OPTIONS = Literal["base", "8b_quantized"]
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T5_ENCODER_MAP: Dict[str, Dict[str, str]] = {
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"base": {
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"text_encoder_repo": "black-forest-labs/FLUX.1-schnell::text_encoder_2",
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"tokenizer_repo": "black-forest-labs/FLUX.1-schnell::tokenizer_2",
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"text_encoder_name": "FLUX.1-schnell_text_encoder_2",
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"tokenizer_name": "FLUX.1-schnell_tokenizer_2",
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"repo": "invokeai/flux_dev::t5_xxl_encoder/base",
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"name": "t5_base_encoder",
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"format": ModelFormat.T5Encoder,
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},
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"8b_quantized": {
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"text_encoder_repo": "hf_repo1",
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"tokenizer_repo": "hf_repo1",
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"text_encoder_name": "hf_repo1",
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"tokenizer_name": "hf_repo1",
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"format": ModelFormat.T5Encoder8b,
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},
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"4b_quantized": {
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"text_encoder_repo": "hf_repo2",
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"tokenizer_repo": "hf_repo2",
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"text_encoder_name": "hf_repo2",
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"tokenizer_name": "hf_repo2",
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"format": ModelFormat.T5Encoder8b,
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"repo": "invokeai/flux_dev::t5_xxl_encoder/8b_quantized",
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"name": "t5_8b_quantized_encoder",
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"format": ModelFormat.T5Encoder,
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},
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}
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@invocation_output("flux_model_loader_output")
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class FluxModelLoaderOutput(BaseInvocationOutput):
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"""Flux base model loader output"""
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@ -189,7 +180,15 @@ class FluxModelLoaderInvocation(BaseInvocation):
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tokenizer2 = self._get_model(context, SubModelType.Tokenizer2)
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clip_encoder = self._get_model(context, SubModelType.TextEncoder)
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t5_encoder = self._get_model(context, SubModelType.TextEncoder2)
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vae = self._install_model(context, SubModelType.VAE, "FLUX.1-schnell_ae", "black-forest-labs/FLUX.1-schnell::ae.safetensors", ModelFormat.Checkpoint, ModelType.VAE, BaseModelType.Flux)
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vae = self._install_model(
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context,
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SubModelType.VAE,
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"FLUX.1-schnell_ae",
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"black-forest-labs/FLUX.1-schnell::ae.safetensors",
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ModelFormat.Checkpoint,
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ModelType.VAE,
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BaseModelType.Flux,
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)
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return FluxModelLoaderOutput(
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transformer=TransformerField(transformer=transformer),
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@ -198,33 +197,59 @@ class FluxModelLoaderInvocation(BaseInvocation):
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vae=VAEField(vae=vae),
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)
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def _get_model(self, context: InvocationContext, submodel:SubModelType) -> ModelIdentifierField:
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match(submodel):
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def _get_model(self, context: InvocationContext, submodel: SubModelType) -> ModelIdentifierField:
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match submodel:
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case SubModelType.Transformer:
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return self.model.model_copy(update={"submodel_type": SubModelType.Transformer})
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case submodel if submodel in [SubModelType.Tokenizer, SubModelType.TextEncoder]:
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return self._install_model(context, submodel, "clip-vit-large-patch14", "openai/clip-vit-large-patch14", ModelFormat.Diffusers, ModelType.CLIPEmbed, BaseModelType.Any)
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case SubModelType.TextEncoder2:
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return self._install_model(context, submodel, T5_ENCODER_MAP[self.t5_encoder]["text_encoder_name"], T5_ENCODER_MAP[self.t5_encoder]["text_encoder_repo"], ModelFormat(T5_ENCODER_MAP[self.t5_encoder]["format"]), ModelType.T5Encoder, BaseModelType.Any)
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case SubModelType.Tokenizer2:
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return self._install_model(context, submodel, T5_ENCODER_MAP[self.t5_encoder]["tokenizer_name"], T5_ENCODER_MAP[self.t5_encoder]["tokenizer_repo"], ModelFormat(T5_ENCODER_MAP[self.t5_encoder]["format"]), ModelType.T5Encoder, BaseModelType.Any)
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return self._install_model(
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context,
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submodel,
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"clip-vit-large-patch14",
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"openai/clip-vit-large-patch14",
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ModelFormat.Diffusers,
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ModelType.CLIPEmbed,
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BaseModelType.Any,
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)
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case submodel if submodel in [SubModelType.Tokenizer2, SubModelType.TextEncoder2]:
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return self._install_model(
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context,
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submodel,
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T5_ENCODER_MAP[self.t5_encoder]["name"],
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T5_ENCODER_MAP[self.t5_encoder]["repo"],
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ModelFormat(T5_ENCODER_MAP[self.t5_encoder]["format"]),
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ModelType.T5Encoder,
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BaseModelType.Any,
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)
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case _:
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raise Exception(f"{submodel.value} is not a supported submodule for a flux model")
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def _install_model(self, context: InvocationContext, submodel:SubModelType, name: str, repo_id: str, format: ModelFormat, type: ModelType, base: BaseModelType):
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if (models := context.models.search_by_attrs(name=name, base=base, type=type)):
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def _install_model(
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self,
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context: InvocationContext,
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submodel: SubModelType,
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name: str,
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repo_id: str,
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format: ModelFormat,
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type: ModelType,
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base: BaseModelType,
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):
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if models := context.models.search_by_attrs(name=name, base=base, type=type):
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if len(models) != 1:
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raise Exception(f"Multiple models detected for selected model with name {name}")
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return ModelIdentifierField.from_config(models[0]).model_copy(update={"submodel_type": submodel})
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else:
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model_path = context.models.download_and_cache_model(repo_id)
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config = ModelRecordChanges(name = name, base = base, type=type, format=format)
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config = ModelRecordChanges(name=name, base=base, type=type, format=format)
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model_install_job = context.models.import_local_model(model_path=model_path, config=config)
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while not model_install_job.in_terminal_state:
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sleep(0.01)
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if not model_install_job.config_out:
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raise Exception(f"Failed to install {name}")
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return ModelIdentifierField.from_config(model_install_job.config_out).model_copy(update={"submodel_type": submodel})
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return ModelIdentifierField.from_config(model_install_job.config_out).model_copy(
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update={"submodel_type": submodel}
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)
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@invocation(
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"main_model_loader",
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@ -301,7 +301,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
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for row in result:
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try:
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model_config = ModelConfigFactory.make_config(json.loads(row[0]), timestamp=row[1])
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except pydantic.ValidationError as e:
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except pydantic.ValidationError:
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# We catch this error so that the app can still run if there are invalid model configs in the database.
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# One reason that an invalid model config might be in the database is if someone had to rollback from a
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# newer version of the app that added a new model type.
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@ -465,18 +465,20 @@ class ModelsInterface(InvocationContextInterface):
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return self._services.model_manager.install.download_and_cache_model(source=source)
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def import_local_model(
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self,
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model_path: Path,
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config: Optional[ModelRecordChanges] = None,
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access_token: Optional[str] = None,
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inplace: Optional[bool] = False,
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self,
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model_path: Path,
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config: Optional[ModelRecordChanges] = None,
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access_token: Optional[str] = None,
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inplace: Optional[bool] = False,
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):
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"""
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TODO: Fill out description of this method
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"""
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if not model_path.exists():
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raise Exception("Models provided to import_local_model must already exist on disk")
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return self._services.model_manager.install.heuristic_import(str(model_path), config=config, access_token=access_token, inplace=inplace)
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return self._services.model_manager.install.heuristic_import(
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str(model_path), config=config, access_token=access_token, inplace=inplace
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)
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def load_local_model(
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self,
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@ -3,9 +3,15 @@ from dataclasses import dataclass
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import torch
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from torch import Tensor, nn
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from invokeai.backend.flux.modules.layers import (DoubleStreamBlock, EmbedND, LastLayer,
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MLPEmbedder, SingleStreamBlock,
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timestep_embedding)
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from invokeai.backend.flux.modules.layers import (
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DoubleStreamBlock,
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EmbedND,
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LastLayer,
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MLPEmbedder,
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SingleStreamBlock,
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timestep_embedding,
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)
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@dataclass
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class FluxParams:
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@ -35,9 +41,7 @@ class Flux(nn.Module):
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self.in_channels = params.in_channels
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self.out_channels = self.in_channels
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if params.hidden_size % params.num_heads != 0:
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raise ValueError(
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f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
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)
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raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
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pe_dim = params.hidden_size // params.num_heads
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if sum(params.axes_dim) != pe_dim:
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raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
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@ -1,5 +1,6 @@
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from torch import Tensor, nn
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from transformers import (PreTrainedModel, PreTrainedTokenizer)
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from transformers import PreTrainedModel, PreTrainedTokenizer
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class HFEncoder(nn.Module):
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def __init__(self, encoder: PreTrainedModel, tokenizer: PreTrainedTokenizer, is_clip: bool, max_length: int):
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@ -36,9 +36,7 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10
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"""
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t = time_factor * t
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half = dim // 2
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freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
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t.device
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)
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freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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134
invokeai/backend/flux/sampling.py
Normal file
134
invokeai/backend/flux/sampling.py
Normal file
@ -0,0 +1,134 @@
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import math
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from typing import Callable
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import torch
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from einops import rearrange, repeat
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from torch import Tensor
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|
||||
from .model import Flux
|
||||
from .modules.conditioner import HFEncoder
|
||||
|
||||
|
||||
def get_noise(
|
||||
num_samples: int,
|
||||
height: int,
|
||||
width: int,
|
||||
device: torch.device,
|
||||
dtype: torch.dtype,
|
||||
seed: int,
|
||||
):
|
||||
return torch.randn(
|
||||
num_samples,
|
||||
16,
|
||||
# allow for packing
|
||||
2 * math.ceil(height / 16),
|
||||
2 * math.ceil(width / 16),
|
||||
device=device,
|
||||
dtype=dtype,
|
||||
generator=torch.Generator(device=device).manual_seed(seed),
|
||||
)
|
||||
|
||||
|
||||
def prepare(t5: HFEncoder, clip: HFEncoder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
|
||||
bs, c, h, w = img.shape
|
||||
if bs == 1 and not isinstance(prompt, str):
|
||||
bs = len(prompt)
|
||||
|
||||
img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
|
||||
if img.shape[0] == 1 and bs > 1:
|
||||
img = repeat(img, "1 ... -> bs ...", bs=bs)
|
||||
|
||||
img_ids = torch.zeros(h // 2, w // 2, 3)
|
||||
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
|
||||
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
|
||||
img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
|
||||
|
||||
if isinstance(prompt, str):
|
||||
prompt = [prompt]
|
||||
txt = t5(prompt)
|
||||
if txt.shape[0] == 1 and bs > 1:
|
||||
txt = repeat(txt, "1 ... -> bs ...", bs=bs)
|
||||
txt_ids = torch.zeros(bs, txt.shape[1], 3)
|
||||
|
||||
vec = clip(prompt)
|
||||
if vec.shape[0] == 1 and bs > 1:
|
||||
vec = repeat(vec, "1 ... -> bs ...", bs=bs)
|
||||
|
||||
return {
|
||||
"img": img,
|
||||
"img_ids": img_ids.to(img.device),
|
||||
"txt": txt.to(img.device),
|
||||
"txt_ids": txt_ids.to(img.device),
|
||||
"vec": vec.to(img.device),
|
||||
}
|
||||
|
||||
|
||||
def time_shift(mu: float, sigma: float, t: Tensor):
|
||||
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
|
||||
|
||||
|
||||
def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
|
||||
m = (y2 - y1) / (x2 - x1)
|
||||
b = y1 - m * x1
|
||||
return lambda x: m * x + b
|
||||
|
||||
|
||||
def get_schedule(
|
||||
num_steps: int,
|
||||
image_seq_len: int,
|
||||
base_shift: float = 0.5,
|
||||
max_shift: float = 1.15,
|
||||
shift: bool = True,
|
||||
) -> list[float]:
|
||||
# extra step for zero
|
||||
timesteps = torch.linspace(1, 0, num_steps + 1)
|
||||
|
||||
# shifting the schedule to favor high timesteps for higher signal images
|
||||
if shift:
|
||||
# eastimate mu based on linear estimation between two points
|
||||
mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
|
||||
timesteps = time_shift(mu, 1.0, timesteps)
|
||||
|
||||
return timesteps.tolist()
|
||||
|
||||
|
||||
def denoise(
|
||||
model: Flux,
|
||||
# model input
|
||||
img: Tensor,
|
||||
img_ids: Tensor,
|
||||
txt: Tensor,
|
||||
txt_ids: Tensor,
|
||||
vec: Tensor,
|
||||
# sampling parameters
|
||||
timesteps: list[float],
|
||||
guidance: float = 4.0,
|
||||
):
|
||||
# this is ignored for schnell
|
||||
guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
|
||||
for t_curr, t_prev in zip(timesteps[:-1], timesteps[1:], strict=False):
|
||||
t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
|
||||
pred = model(
|
||||
img=img,
|
||||
img_ids=img_ids,
|
||||
txt=txt,
|
||||
txt_ids=txt_ids,
|
||||
y=vec,
|
||||
timesteps=t_vec,
|
||||
guidance=guidance_vec,
|
||||
)
|
||||
|
||||
img = img + (t_prev - t_curr) * pred
|
||||
|
||||
return img
|
||||
|
||||
|
||||
def unpack(x: Tensor, height: int, width: int) -> Tensor:
|
||||
return rearrange(
|
||||
x,
|
||||
"b (h w) (c ph pw) -> b c (h ph) (w pw)",
|
||||
h=math.ceil(height / 16),
|
||||
w=math.ceil(width / 16),
|
||||
ph=2,
|
||||
pw=2,
|
||||
)
|
||||
@ -1,14 +1,17 @@
|
||||
# Copyright (c) 2024, Brandon W. Rising and the InvokeAI Development Team
|
||||
"""Class for Flux model loading in InvokeAI."""
|
||||
|
||||
from pathlib import Path
|
||||
import yaml
|
||||
|
||||
from dataclasses import fields
|
||||
from safetensors.torch import load_file
|
||||
from typing import Optional, Any
|
||||
from transformers import T5EncoderModel, T5Tokenizer
|
||||
from pathlib import Path
|
||||
from typing import Any, Optional
|
||||
|
||||
import yaml
|
||||
from safetensors.torch import load_file
|
||||
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
|
||||
|
||||
from invokeai.app.services.config.config_default import get_config
|
||||
from invokeai.backend.flux.model import Flux, FluxParams
|
||||
from invokeai.backend.flux.modules.autoencoder import AutoEncoder, AutoEncoderParams
|
||||
from invokeai.backend.model_manager import (
|
||||
AnyModel,
|
||||
AnyModelConfig,
|
||||
@ -19,20 +22,15 @@ from invokeai.backend.model_manager import (
|
||||
)
|
||||
from invokeai.backend.model_manager.config import (
|
||||
CheckpointConfigBase,
|
||||
MainCheckpointConfig,
|
||||
CLIPEmbedDiffusersConfig,
|
||||
MainCheckpointConfig,
|
||||
T5EncoderConfig,
|
||||
VAECheckpointConfig,
|
||||
)
|
||||
from invokeai.app.services.config.config_default import get_config
|
||||
from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
|
||||
from invokeai.backend.model_manager.load.model_loaders.generic_diffusers import GenericDiffusersLoader
|
||||
from invokeai.backend.util.silence_warnings import SilenceWarnings
|
||||
from invokeai.backend.util.devices import TorchDevice
|
||||
from invokeai.backend.flux.model import Flux, FluxParams
|
||||
from invokeai.backend.flux.modules.autoencoder import AutoEncoderParams, AutoEncoder
|
||||
from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
|
||||
T5Tokenizer)
|
||||
from invokeai.backend.util.silence_warnings import SilenceWarnings
|
||||
|
||||
app_config = get_config()
|
||||
|
||||
@ -58,7 +56,7 @@ class FluxVAELoader(GenericDiffusersLoader):
|
||||
raise
|
||||
|
||||
dataclass_fields = {f.name for f in fields(AutoEncoderParams)}
|
||||
filtered_data = {k: v for k, v in flux_conf['params']['ae_params'].items() if k in dataclass_fields}
|
||||
filtered_data = {k: v for k, v in flux_conf["params"]["ae_params"].items() if k in dataclass_fields}
|
||||
params = AutoEncoderParams(**filtered_data)
|
||||
|
||||
with SilenceWarnings():
|
||||
@ -92,6 +90,7 @@ class ClipCheckpointModel(GenericDiffusersLoader):
|
||||
|
||||
raise Exception("Only Checkpoint Flux models are currently supported.")
|
||||
|
||||
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T5Encoder, format=ModelFormat.T5Encoder)
|
||||
class T5EncoderCheckpointModel(GenericDiffusersLoader):
|
||||
"""Class to load main models."""
|
||||
@ -106,9 +105,9 @@ class T5EncoderCheckpointModel(GenericDiffusersLoader):
|
||||
|
||||
match submodel_type:
|
||||
case SubModelType.Tokenizer2:
|
||||
return T5Tokenizer.from_pretrained(Path(config.path), max_length=512)
|
||||
return T5Tokenizer.from_pretrained(Path(config.path) / "encoder", max_length=512)
|
||||
case SubModelType.TextEncoder2:
|
||||
return T5EncoderModel.from_pretrained(Path(config.path))
|
||||
return T5EncoderModel.from_pretrained(Path(config.path) / "tokenizer")
|
||||
|
||||
raise Exception("Only Checkpoint Flux models are currently supported.")
|
||||
|
||||
@ -148,7 +147,7 @@ class FluxCheckpointModel(GenericDiffusersLoader):
|
||||
params = None
|
||||
model_path = Path(config.path)
|
||||
dataclass_fields = {f.name for f in fields(FluxParams)}
|
||||
filtered_data = {k: v for k, v in flux_conf['params'].items() if k in dataclass_fields}
|
||||
filtered_data = {k: v for k, v in flux_conf["params"].items() if k in dataclass_fields}
|
||||
params = FluxParams(**filtered_data)
|
||||
|
||||
with SilenceWarnings():
|
||||
|
||||
@ -39,11 +39,15 @@ VARIANT_TO_IN_CHANNEL_MAP = {
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Diffusers)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Diffusers)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Diffusers)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers)
|
||||
@ModelLoaderRegistry.register(
|
||||
base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Diffusers
|
||||
)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion1, type=ModelType.Main, format=ModelFormat.Checkpoint)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusion2, type=ModelType.Main, format=ModelFormat.Checkpoint)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXL, type=ModelType.Main, format=ModelFormat.Checkpoint)
|
||||
@ModelLoaderRegistry.register(base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Checkpoint)
|
||||
@ModelLoaderRegistry.register(
|
||||
base=BaseModelType.StableDiffusionXLRefiner, type=ModelType.Main, format=ModelFormat.Checkpoint
|
||||
)
|
||||
class StableDiffusionDiffusersModel(GenericDiffusersLoader):
|
||||
"""Class to load main models."""
|
||||
|
||||
|
||||
@ -9,7 +9,7 @@ from typing import Optional
|
||||
import torch
|
||||
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
||||
from diffusers.schedulers.scheduling_utils import SchedulerMixin
|
||||
from transformers import CLIPTokenizer, T5TokenizerFast, T5Tokenizer
|
||||
from transformers import CLIPTokenizer, T5Tokenizer, T5TokenizerFast
|
||||
|
||||
from invokeai.backend.image_util.depth_anything.depth_anything_pipeline import DepthAnythingPipeline
|
||||
from invokeai.backend.image_util.grounding_dino.grounding_dino_pipeline import GroundingDinoPipeline
|
||||
@ -52,7 +52,10 @@ def calc_model_size_by_data(logger: logging.Logger, model: AnyModel) -> int:
|
||||
return model.calc_size()
|
||||
elif isinstance(
|
||||
model,
|
||||
(T5TokenizerFast,T5Tokenizer,),
|
||||
(
|
||||
T5TokenizerFast,
|
||||
T5Tokenizer,
|
||||
),
|
||||
):
|
||||
return len(model)
|
||||
else:
|
||||
|
||||
@ -56,7 +56,7 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched
|
||||
},
|
||||
BaseModelType.StableDiffusionXLRefiner: {
|
||||
ModelVariantType.Normal: "sd_xl_refiner.yaml",
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@ -323,7 +323,7 @@ class ModelProbe(object):
|
||||
|
||||
if model_type is ModelType.Main:
|
||||
if base_type == BaseModelType.Flux:
|
||||
config_file="flux/flux1-schnell.yaml"
|
||||
config_file = "flux/flux1-schnell.yaml"
|
||||
else:
|
||||
config_file = LEGACY_CONFIGS[base_type][variant_type]
|
||||
if isinstance(config_file, dict): # need another tier for sd-2.x models
|
||||
@ -727,6 +727,7 @@ class T5EncoderFolderProbe(FolderProbeBase):
|
||||
def get_format(self) -> ModelFormat:
|
||||
return ModelFormat.T5Encoder
|
||||
|
||||
|
||||
class ONNXFolderProbe(PipelineFolderProbe):
|
||||
def get_base_type(self) -> BaseModelType:
|
||||
# Due to the way the installer is set up, the configuration file for safetensors
|
||||
|
||||
Loading…
Reference in New Issue
Block a user