diff --git a/.pylintrc b/.pylintrc index b9c4e76f8..bef8906ce 100644 --- a/.pylintrc +++ b/.pylintrc @@ -21,6 +21,7 @@ ignore-paths=/usr/lib/.*$, modules/intel/ipex, modules/intel/openvino, modules/k-diffusion, + modules/flex2, modules/ldsr, modules/meissonic, modules/mod, diff --git a/.ruff.toml b/.ruff.toml index 312717ae4..96fd1323a 100644 --- a/.ruff.toml +++ b/.ruff.toml @@ -8,6 +8,7 @@ exclude = [ "modules/control/proc", "modules/control/units", "modules/freescale", + "modules/flex2", "modules/ggml", "modules/hidiffusion", "modules/hijack", diff --git a/CHANGELOG.md b/CHANGELOG.md index 83506cc98..4edd0565f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,10 +1,11 @@ # Change Log for SD.Next -## Update for 2025-04-23 +## Update for 2025-04-24 - **Features** - [Nunchaku](https://github.com/mit-han-lab/nunchaku) inference engine with custom **SVDQuant** 4-bit execution highly experimental and with limited support, but when it works, its magic: **Flux.1 at 6.0 it/s** *(not sec/it)*! + basically, it can speed up supported models by 2-5x by using custom quantization and execution engine see [Nunchaku Wiki](https://github.com/vladmandic/sdnext/wiki/Nunchaku) for installation guide and list of supported models & features - [FramePack](https://github.com/vladmandic/sd-extension-framepack) based on **HunyuanVideo-I2V** full support and much more for **Lllyasviel** [FramePack](https://lllyasviel.github.io/frame_pack_gitpage/) @@ -16,6 +17,10 @@ - custom models: e.g. replace llama with one of your choice - multiple video codecs and with hw acceleration, raw export, frame export, frame interpolation - quantization support, new offloading, more configuration options, cross-platform, etc. + - [Ostris Flex.2 Preview](https://huggingface.co/ostris/Flex.2-preview) + more than a FLUX.1 finetune, FLEX.2 is created from *Flux.1 Schnell -> OpenFlux.1 -> Flex.1-alpha -> Flex.2-preview* + and it has universal control and inpainting support built in! + available via *networks -> models -> reference* - [LTXVideo 0.9.6](https://github.com/Lightricks/LTX-Video?tab=readme-ov-file) **T2V** and **I2V** in both **Standard** and **Distilled** variants available in *video tab* diff --git a/html/reference.json b/html/reference.json index 1eb29f032..822c64598 100644 --- a/html/reference.json +++ b/html/reference.json @@ -180,6 +180,13 @@ "extras": "sampler: Default, cfg_scale: 3.5" }, + "Ostris Flex.2 Preview": { + "path": "ostris/Flex.2-preview", + "preview": "ostris--Flex.2-preview.jpg", + "desc": "Open Source 8B parameter Text to Image Diffusion Model with universal control and inpainting support built in. Early access preview release. The next version of Flex.1-alpha", + "skip": true, + "extras": "sampler: Default, cfg_scale: 3.5" + }, "Ostris Flex.1 Alpha": { "path": "ostris/Flex.1-alpha", "preview": "ostris--Flex.1-alpha.jpg", diff --git a/models/Reference/ostris--Flex.2-preview.jpg b/models/Reference/ostris--Flex.2-preview.jpg new file mode 100644 index 000000000..1635f36c8 Binary files /dev/null and b/models/Reference/ostris--Flex.2-preview.jpg differ diff --git a/modules/flex2/__init__.py b/modules/flex2/__init__.py new file mode 100644 index 000000000..6c452551f --- /dev/null +++ b/modules/flex2/__init__.py @@ -0,0 +1,435 @@ +from diffusers import FluxControlPipeline, FluxTransformer2DModel +from typing import Any, Callable, Dict, List, Optional, Union +import torch + +from diffusers.image_processor import PipelineImageInput +import numpy as np +import torch.nn.functional as F +from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput +from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps, XLA_AVAILABLE + + +class Flex2Pipeline(FluxControlPipeline): + def __init__( + self, + scheduler, + vae, + text_encoder, + tokenizer, + text_encoder_2, + tokenizer_2, + transformer, + ): + super().__init__(scheduler, vae, text_encoder, tokenizer, text_encoder_2, tokenizer_2, transformer) + + def check_inputs( + self, + prompt, + prompt_2, + height, + width, + prompt_embeds=None, + pooled_prompt_embeds=None, + callback_on_step_end_tensor_inputs=None, + max_sequence_length=None, + inpaint_image=None, + inpaint_mask=None, + control_image=None, + ): + super().check_inputs( + prompt, + prompt_2, + height, + width, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + max_sequence_length=max_sequence_length, + ) + if inpaint_image is not None and inpaint_mask is None: + raise ValueError( + "If `inpaint_image` is passed, `inpaint_mask` must be passed as well. " + "Please make sure to pass both `inpaint_image` and `inpaint_mask`." + ) + if inpaint_mask is not None and inpaint_image is None: + raise ValueError( + "If `inpaint_mask` is passed, `inpaint_image` must be passed as well. " + "Please make sure to pass both `inpaint_image` and `inpaint_mask`." + ) + + @torch.no_grad() + def __call__( + self, + prompt: Union[str, List[str]] = None, + prompt_2: Optional[Union[str, List[str]]] = None, + inpaint_image: Optional[PipelineImageInput] = None, + inpaint_mask: Optional[PipelineImageInput] = None, + control_image: Optional[PipelineImageInput] = None, + control_strength: Optional[float] = 1.0, + control_stop: Optional[float] = 1.0, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 28, + sigmas: Optional[List[float]] = None, + guidance_scale: float = 3.5, + num_images_per_prompt: Optional[int] = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + joint_attention_kwargs: Optional[Dict[str, Any]] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + max_sequence_length: int = 512, + **kwargs, + ): + r""" + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is + will be used instead + inpaint_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: + `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): + The image to be inpainted. + inpaint_mask (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: + `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): + A black and white mask to be used for inpainting. The white pixels are the areas to be inpainted, while the + black pixels are the areas to be kept. + control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: + `List[List[torch.Tensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): + The control image (line, depth, pose, etc.) to be used for the generation. The control image + control_strength (`float`, *optional*, defaults to 1.0): + The strength of the control image. The higher the value, the more the control image will be used to + guide the generation. The lower the value, the less the control image will be used to guide the + generation. + control_stop (`float`, *optional*, defaults to 1.0): + The percentage of the generation to drop out the control. 0.0 to 1.0. 0.5 mean the control will be dropped + out at 50% of the generation. + height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The height in pixels of the generated image. This is set to 1024 by default for the best results. + width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): + The width in pixels of the generated image. This is set to 1024 by default for the best results. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + guidance_scale (`float`, *optional*, defaults to 3.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. + If not provided, pooled text embeddings will be generated from `prompt` input argument. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple. + joint_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. + + Examples: + + Returns: + [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict` + is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated + images. + """ + + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_sample_size * self.vae_scale_factor + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + prompt_2, + height, + width, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, + max_sequence_length=max_sequence_length, + ) + + self._guidance_scale = guidance_scale + self._joint_attention_kwargs = joint_attention_kwargs + self._interrupt = False + + # 2. Define call parameters + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # 3. Prepare text embeddings + lora_scale = ( + self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None + ) + ( + prompt_embeds, + pooled_prompt_embeds, + text_ids, + ) = self.encode_prompt( + prompt=prompt, + prompt_2=prompt_2, + prompt_embeds=prompt_embeds, + pooled_prompt_embeds=pooled_prompt_embeds, + device=device, + num_images_per_prompt=num_images_per_prompt, + max_sequence_length=max_sequence_length, + lora_scale=lora_scale, + ) + + # 4. Prepare latent variables + num_channels_latents = self.transformer.config.in_channels // 4 + + # only prepare latents for non controls + # (16 + 1 + 16 ) + num_control_channels = 33 + num_channels_latents = num_channels_latents - num_control_channels + + control_latents = None + inpaint_latents = None + inpaint_latents_mask = None + + latent_height = height // self.vae_scale_factor + latent_width = width // self.vae_scale_factor + + # process the control and inpaint channels + + if control_image is None: + control_latents = torch.zeros( + batch_size * num_images_per_prompt, + 16, + latent_height, + latent_width, + device=device, + dtype=self.vae.dtype, + ) + else: + control_image = self.prepare_image( + image=control_image, + width=width, + height=height, + batch_size=batch_size * num_images_per_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + dtype=self.vae.dtype, + ) + control_image = self.vae.encode(control_image).latent_dist.sample(generator=generator) + control_latents = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor + + # apply control strength + control_latents = control_latents * control_strength + + if inpaint_image is None and inpaint_mask is None: + inpaint_latents = torch.zeros( + batch_size * num_images_per_prompt, + 16, + latent_height, + latent_width, + device=device, + dtype=self.vae.dtype, + ) + inpaint_latents_mask = torch.ones( + batch_size * num_images_per_prompt, + 1, + latent_height, + latent_width, + device=device, + dtype=self.vae.dtype, + ) + else: + inpaint_image = self.prepare_image( + image=inpaint_image, + width=width, + height=height, + batch_size=batch_size * num_images_per_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + dtype=self.vae.dtype, + ) + inpaint_image = self.vae.encode(inpaint_image).latent_dist.sample(generator=generator) + inpaint_latents = (inpaint_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor + height_inpaint_image, width_inpaint_image = control_image.shape[2:] + + inpaint_mask = self.prepare_image( + image=inpaint_mask, + width=width, + height=height, + batch_size=batch_size * num_images_per_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + dtype=self.vae.dtype, + ) + # mask is 3 ch -1 to 1. make it 1ch, 0 to 1 + inpaint_mask = inpaint_mask[:, 0:1, :, :] * 0.5 + 0.5 + # resize to match height_inpaint_image and width_inpaint_image + inpaint_latents_mask = F.interpolate(inpaint_mask, size=(height_inpaint_image, width_inpaint_image), mode="bilinear", align_corners=False) + + # apply inverted mask to inpaint latents + inpaint_latents = inpaint_latents * (1 - inpaint_latents_mask) + + # concat the latent controls on the channel dimension every step + latent_controls = torch.cat([inpaint_latents, inpaint_latents_mask, control_latents], dim=1) + latent_no_controls = torch.cat([inpaint_latents, inpaint_latents_mask, torch.zeros_like(control_latents)], dim=1) + + # pack the controls + height_latent_controls, width_latent_controls = latent_controls.shape[2:] + packed_latent_controls = self._pack_latents( + latent_controls, + batch_size * num_images_per_prompt, + num_control_channels, + height_latent_controls, + width_latent_controls, + ) + packed_latent_no_controls = self._pack_latents( + latent_no_controls, + batch_size * num_images_per_prompt, + num_control_channels, + height_latent_controls, + width_latent_controls, + ) + + latents, latent_image_ids = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 5. Prepare timesteps + sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas + image_seq_len = latents.shape[1] + mu = calculate_shift( + image_seq_len, + self.scheduler.config.get("base_image_seq_len", 256), + self.scheduler.config.get("max_image_seq_len", 4096), + self.scheduler.config.get("base_shift", 0.5), + self.scheduler.config.get("max_shift", 1.15), + ) + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, + num_inference_steps, + device, + sigmas=sigmas, + mu=mu, + ) + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + self._num_timesteps = len(timesteps) + + # handle guidance + if self.transformer.config.guidance_embeds: + guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32) + guidance = guidance.expand(latents.shape[0]) + else: + guidance = None + + control_cutoff = int(len(timesteps) * control_stop) + + # 6. Denoising loop + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + control_latents = packed_latent_controls if i < control_cutoff else packed_latent_no_controls + + latent_model_input = torch.cat([latents, control_latents], dim=2) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latents.shape[0]).to(latents.dtype) + + noise_pred = self.transformer( + hidden_states=latent_model_input, + timestep=timestep / 1000, + guidance=guidance, + pooled_projections=pooled_prompt_embeds, + encoder_hidden_states=prompt_embeds, + txt_ids=text_ids, + img_ids=latent_image_ids, + joint_attention_kwargs=self.joint_attention_kwargs, + return_dict=False, + )[0] + + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] + + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if output_type == "latent": + image = latents + else: + latents = self._unpack_latents(latents, height, width, self.vae_scale_factor) + latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor + image = self.vae.decode(latents, return_dict=False)[0] + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return FluxPipelineOutput(images=image) diff --git a/modules/model_flex.py b/modules/model_flex.py new file mode 100644 index 000000000..721ac785a --- /dev/null +++ b/modules/model_flex.py @@ -0,0 +1,88 @@ +import os +import transformers +import diffusers +from huggingface_hub import auth_check +from modules import shared, devices, sd_models, model_quant, modelloader, sd_hijack_te + + +def load_transformer(repo_id, diffusers_load_config={}): + load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Transformer', device_map=True) + fn = None + + if shared.opts.sd_unet is not None and shared.opts.sd_unet != 'Default': + from modules import sd_unet + if shared.opts.sd_unet not in list(sd_unet.unet_dict): + shared.log.error(f'Load module: type=Transformer not found: {shared.opts.sd_unet}') + return None + fn = sd_unet.unet_dict[shared.opts.sd_unet] if os.path.exists(sd_unet.unet_dict[shared.opts.sd_unet]) else None + + if fn is not None and 'gguf' in fn.lower(): + shared.log.error('Load model: type=HiDream format="gguf" unsupported') + transformer = None + from modules import ggml + transformer = ggml.load_gguf(fn, cls=diffusers.HiDreamImageTransformer2DModel, compute_dtype=devices.dtype) + elif fn is not None and 'safetensors' in fn.lower(): + shared.log.debug(f'Load model: type=FLEX transformer="{repo_id}" quant="{model_quant.get_quant(repo_id)}" args={load_args}') + transformer = diffusers.FluxTransformer2DModel.from_single_file(fn, cache_dir=shared.opts.hfcache_dir, **load_args) + # elif model_quant.check_nunchaku('Transformer'): + # shared.log.error(f'Load model: type=HiDream transformer="{repo_id}" quant="Nunchaku" unsupported') + # transformer = None + else: + shared.log.debug(f'Load model: type=FLEX transformer="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}') + transformer = diffusers.FluxTransformer2DModel.from_pretrained( + repo_id, + subfolder="transformer", + cache_dir=shared.opts.hfcache_dir, + **load_args, + **quant_args, + ) + if shared.opts.diffusers_offload_mode != 'none' and transformer is not None: + sd_models.move_model(transformer, devices.cpu) + return transformer + + +def load_text_encoders(repo_id, diffusers_load_config={}): + load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True) + shared.log.debug(f'Load model: type=FLEX t5="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}') + text_encoder_2 = transformers.T5EncoderModel.from_pretrained( + repo_id, + subfolder="text_encoder_2", + cache_dir=shared.opts.hfcache_dir, + **load_args, + **quant_args, + ) + if shared.opts.diffusers_offload_mode != 'none' and text_encoder_2 is not None: + sd_models.move_model(text_encoder_2, devices.cpu) + return text_encoder_2 + + +def load_flex(checkpoint_info, diffusers_load_config={}): + repo_id = sd_models.path_to_repo(checkpoint_info.name) + login = modelloader.hf_login() + try: + auth_check(repo_id) + except Exception as e: + shared.log.error(f'Load model: repo="{repo_id}" login={login} {e}') + return False + + transformer = load_transformer(repo_id, diffusers_load_config) + text_encoder_2 = load_text_encoders(repo_id, diffusers_load_config) + + load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model') + shared.log.debug(f'Load model: type=FLEX model="{checkpoint_info.name}" repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}') + + from modules.flex2 import Flex2Pipeline + pipe = Flex2Pipeline.from_pretrained( + repo_id, + # custom_pipeline=repo_id, + transformer=transformer, + text_encoder_2=text_encoder_2, + cache_dir=shared.opts.diffusers_dir, + **load_args, + ) + sd_hijack_te.init_hijack(pipe) + del text_encoder_2 + del transformer + + devices.torch_gc() + return pipe diff --git a/modules/modeldata.py b/modules/modeldata.py index 6f3314644..ae3e560ad 100644 --- a/modules/modeldata.py +++ b/modules/modeldata.py @@ -27,7 +27,7 @@ def get_model_type(pipe): model_type = 'sc' elif "AuraFlow" in name: model_type = 'auraflow' - elif "Flux" in name: + elif "Flux" in name or "Flex.1" or "Flex.2": model_type = 'f1' elif "Lumina2" in name: model_type = 'lumina2' diff --git a/modules/modelloader.py b/modules/modelloader.py index 8c5932477..7bbb4f341 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -379,12 +379,16 @@ def get_reference_opts(name: str, quiet=False): model_opts = {} name = name.replace('Diffusers/', 'huggingface/') for k, v in shared.reference_models.items(): - model_name = os.path.splitext(v.get('path', '').split('@')[0])[0] + model_name = v.get('path', '') if k == name or model_name == name: model_opts = v break - model_name = model_name.replace('huggingface/', '') - if k == name or model_name == name: + model_name_split = os.path.splitext(model_name.split('@')[0])[0] + if k == name or model_name_split == name: + model_opts = v + break + model_name_replace = model_name.replace('huggingface/', '') + if k == name or model_name_replace == name: model_opts = v break if not model_opts: diff --git a/modules/sd_detect.py b/modules/sd_detect.py index 5ba1293af..4a40453cd 100644 --- a/modules/sd_detect.py +++ b/modules/sd_detect.py @@ -96,6 +96,8 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False): guess = 'FLUX' if size > 11000 and size < 16000: warn(f'Model detected as FLUX UNET model, but attempting to load a base model: {op}={f} size={size} MB') + if 'flex.2' in f.lower(): + guess = 'FLEX' # guess for diffusers index = os.path.join(f, 'model_index.json') if os.path.exists(index) and os.path.isfile(index): @@ -103,7 +105,7 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False): cls = index.get('_class_name', None) if cls is not None: pipeline = getattr(diffusers, cls) - if 'Flux' in pipeline.__name__: + if 'Flux' in pipeline.__name__ and guess != 'FLEX': guess = 'FLUX' if 'StableDiffusion3' in pipeline.__name__: guess = 'Stable Diffusion 3' diff --git a/modules/sd_models.py b/modules/sd_models.py index 510fff7ea..4e7664d6d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -309,6 +309,9 @@ def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op=' elif model_type in ['FLUX']: from modules.model_flux import load_flux sd_model = load_flux(checkpoint_info, diffusers_load_config) + elif model_type in ['FLEX']: + from modules.model_flex import load_flex + sd_model = load_flex(checkpoint_info, diffusers_load_config) elif model_type in ['Lumina 2']: from modules.model_lumina import load_lumina2 sd_model = load_lumina2(checkpoint_info, diffusers_load_config) diff --git a/modules/shared_items.py b/modules/shared_items.py index f51609a7d..4e818fec1 100644 --- a/modules/shared_items.py +++ b/modules/shared_items.py @@ -20,6 +20,7 @@ pipelines = { 'HunyuanDiT': getattr(diffusers, 'HunyuanDiTPipeline', None), 'DeepFloyd IF': getattr(diffusers, 'IFPipeline', None), 'FLUX': getattr(diffusers, 'FluxPipeline', None), + 'FLEX': getattr(diffusers, 'AutoPipelineForText2Image', None), 'Sana': getattr(diffusers, 'SanaPipeline', None), 'Lumina-Next': getattr(diffusers, 'LuminaText2ImgPipeline', None), 'Lumina 2': getattr(diffusers, 'Lumina2Text2ImgPipeline', None), diff --git a/wiki b/wiki index 3c26f7806..f07c96ffc 160000 --- a/wiki +++ b/wiki @@ -1 +1 @@ -Subproject commit 3c26f780697422f2fc296273d5a90416c941067a +Subproject commit f07c96ffce44bbec5b89fbd7960354fec770e64b