From cce7cdd65b5bf283c1a39c29f2726902a3c135ca Mon Sep 17 00:00:00 2001 From: yanzewu Date: Thu, 12 Sep 2024 14:56:44 +0800 Subject: [PATCH] release PuLID-FLUX-v0.9.0 --- README.md | 6 +- app.py | 2 +- app_flux.py | 292 ++++++++++++++++++++++ docs/pulid_for_flux.md | 46 ++++ v1.1_preview.md => docs/v1.1_preview.md | 0 example_inputs/pengwei.jpg | Bin 0 -> 1244849 bytes flux/__init__.py | 11 + flux/__main__.py | 4 + flux/api.py | 194 +++++++++++++++ flux/cli.py | 261 ++++++++++++++++++++ flux/math.py | 31 +++ flux/model.py | 135 ++++++++++ flux/modules/__init__.py | 0 flux/modules/autoencoder.py | 312 ++++++++++++++++++++++++ flux/modules/conditioner.py | 37 +++ flux/modules/layers.py | 253 +++++++++++++++++++ flux/sampling.py | 161 ++++++++++++ flux/util.py | 201 +++++++++++++++ pulid/encoders_flux.py | 207 ++++++++++++++++ pulid/pipeline.py | 2 +- pulid/pipeline_flux.py | 186 ++++++++++++++ requirements.txt | 5 +- 22 files changed, 2342 insertions(+), 4 deletions(-) create mode 100644 app_flux.py create mode 100644 docs/pulid_for_flux.md rename v1.1_preview.md => docs/v1.1_preview.md (100%) create mode 100644 example_inputs/pengwei.jpg create mode 100644 flux/__init__.py create mode 100644 flux/__main__.py create mode 100644 flux/api.py create mode 100644 flux/cli.py create mode 100644 flux/math.py create mode 100644 flux/model.py create mode 100644 flux/modules/__init__.py create mode 100644 flux/modules/autoencoder.py create mode 100644 flux/modules/conditioner.py create mode 100644 flux/modules/layers.py create mode 100644 flux/sampling.py create mode 100644 flux/util.py create mode 100644 pulid/encoders_flux.py create mode 100644 pulid/pipeline_flux.py diff --git a/README.md b/README.md index 3e3968a..866c47e 100644 --- a/README.md +++ b/README.md @@ -7,10 +7,14 @@ > ByteDance Inc
### :triangular_flag_on_post: Updates -* **2024.05.23**: 🔥🔥share the [preview of our upcoming v1.1 model](v1.1_preview.md), please stay tuned +* **2024.09.12**: 💥 We're thrilled to announce the release of the **PuLID-FLUX-v0.9.0 model**. Enjoy exploring its capabilities! 😊 [Learn more](docs/pulid_for_flux.md). +* **2024.05.23**: share the [preview of our upcoming v1.1 model](docs/v1.1_preview.md), please stay tuned * **2024.05.01**: release v1 codes&models, also the [🤗HuggingFace Demo](https://huggingface.co/spaces/yanze/PuLID) * **2024.04.25**: release arXiv paper. +## PuLID for FLUX +Please check the doc and demo of PuLID-FLUX [here](docs/pulid_for_flux.md). + ## Examples Images generated with our PuLID ![examples](https://github.com/ToTheBeginning/PuLID/assets/11482921/65610b0d-ba4f-4dc3-a74d-bd60f8f5ce37) diff --git a/app.py b/app.py index 0c8abdd..6be73b6 100644 --- a/app.py +++ b/app.py @@ -159,7 +159,7 @@ def run(*args): example_inps = [ [ - 'portrait, flat papercut style, silhouette, clean cuts, paper, sharp edges, minimalist,color block,man', + 'portrait, flat papercut style, silhouette, clean cuts, paper, sharp edges, minimalist,color block,man', # noqa E501 'example_inputs/lecun.jpg', 'fidelity', ] diff --git a/app_flux.py b/app_flux.py new file mode 100644 index 0000000..de47ccf --- /dev/null +++ b/app_flux.py @@ -0,0 +1,292 @@ +import time + +import gradio as gr +import torch +from einops import rearrange +from PIL import Image + +from flux.cli import SamplingOptions +from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack +from flux.util import load_ae, load_clip, load_flow_model, load_t5 +from pulid.pipeline_flux import PuLIDPipeline +from pulid.utils import resize_numpy_image_long + + +def get_models(name: str, device: torch.device, offload: bool): + t5 = load_t5(device, max_length=128) + clip = load_clip(device) + model = load_flow_model(name, device="cpu" if offload else device) + model.eval() + ae = load_ae(name, device="cpu" if offload else device) + return model, ae, t5, clip + + +class FluxGenerator: + def __init__(self, model_name: str, device: str, offload: bool, args): + self.device = torch.device(device) + self.offload = offload + self.model_name = model_name + self.model, self.ae, self.t5, self.clip = get_models( + model_name, + device=self.device, + offload=self.offload, + ) + self.pulid_model = PuLIDPipeline(self.model, device, weight_dtype=torch.bfloat16) + self.pulid_model.load_pretrain(args.pretrained_model) + + @torch.inference_mode() + def generate_image( + self, + width, + height, + num_steps, + start_step, + guidance, + seed, + prompt, + id_image=None, + id_weight=1.0, + neg_prompt="", + true_cfg=1.0, + timestep_to_start_cfg=1, + max_sequence_length=128, + ): + self.t5.max_length = max_sequence_length + + seed = int(seed) + if seed == -1: + seed = None + + opts = SamplingOptions( + prompt=prompt, + width=width, + height=height, + num_steps=num_steps, + guidance=guidance, + seed=seed, + ) + + if opts.seed is None: + opts.seed = torch.Generator(device="cpu").seed() + print(f"Generating '{opts.prompt}' with seed {opts.seed}") + t0 = time.perf_counter() + + use_true_cfg = abs(true_cfg - 1.0) > 1e-2 + + if id_image is not None: + id_image = resize_numpy_image_long(id_image, 1024) + id_embeddings, uncond_id_embeddings = self.pulid_model.get_id_embedding(id_image, cal_uncond=use_true_cfg) + else: + id_embeddings = None + uncond_id_embeddings = None + + # prepare input + x = get_noise( + 1, + opts.height, + opts.width, + device=self.device, + dtype=torch.bfloat16, + seed=opts.seed, + ) + timesteps = get_schedule( + opts.num_steps, + x.shape[-1] * x.shape[-2] // 4, + shift=True, + ) + + if self.offload: + self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device) + inp = prepare(t5=self.t5, clip=self.clip, img=x, prompt=opts.prompt) + inp_neg = prepare(t5=self.t5, clip=self.clip, img=x, prompt=neg_prompt) if use_true_cfg else None + + # offload TEs to CPU, load model to gpu + if self.offload: + self.t5, self.clip = self.t5.cpu(), self.clip.cpu() + torch.cuda.empty_cache() + self.model = self.model.to(self.device) + + # denoise initial noise + x = denoise( + self.model, **inp, timesteps=timesteps, guidance=opts.guidance, id=id_embeddings, id_weight=id_weight, + start_step=start_step, uncond_id=uncond_id_embeddings, true_cfg=true_cfg, + timestep_to_start_cfg=timestep_to_start_cfg, + neg_txt=inp_neg["txt"] if use_true_cfg else None, + neg_txt_ids=inp_neg["txt_ids"] if use_true_cfg else None, + neg_vec=inp_neg["vec"] if use_true_cfg else None, + ) + + # offload model, load autoencoder to gpu + if self.offload: + self.model.cpu() + torch.cuda.empty_cache() + self.ae.decoder.to(x.device) + + # decode latents to pixel space + x = unpack(x.float(), opts.height, opts.width) + with torch.autocast(device_type=self.device.type, dtype=torch.bfloat16): + x = self.ae.decode(x) + + if self.offload: + self.ae.decoder.cpu() + torch.cuda.empty_cache() + + t1 = time.perf_counter() + + print(f"Done in {t1 - t0:.1f}s.") + # bring into PIL format + x = x.clamp(-1, 1) + # x = embed_watermark(x.float()) + x = rearrange(x[0], "c h w -> h w c") + + img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy()) + return img, str(opts.seed), self.pulid_model.debug_img_list + +_HEADER_ = ''' +
+

PuLID for FLUX

+

Paper: PuLID: Pure and Lightning ID Customization via Contrastive Alignment | Codes: GitHub

+
+ +❗️❗️❗️**Tips:** +- `timestep to start inserting ID:` The smaller the value, the higher the fidelity, but the lower the editability; the higher the value, the lower the fidelity, but the higher the editability. **The recommended range for this value is between 0 and 4**. For photorealistic scenes, we recommend using 4; for stylized scenes, we recommend using 0-1. If you are not satisfied with the similarity, you can lower this value; conversely, if you are not satisfied with the editability, you can increase this value. +- `true CFG scale:` In most scenarios, it is recommended to use a fake CFG, i.e., setting the true CFG scale to 1, and just adjusting the guidance scale. This is also more efficiency. However, in a few cases, utilizing a true CFG can yield better results. For more detaileds, please refer to XX. +- please refer to the github doc for more details and info about the model, we provide the detail explanation about the above two parameters in the doc. +- we provide some examples in the bottom, you can try these example prompts first + +''' # noqa E501 + +_CITE_ = r""" +If PuLID is helpful, please help to ⭐ the Github Repo. Thanks! +--- + +📧 **Contact** +If you have any questions or feedbacks, feel free to open a discussion or contact wuyanze123@gmail.com. +""" # noqa E501 + + +def create_demo(args, model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu", + offload: bool = False): + generator = FluxGenerator(model_name, device, offload, args) + + with gr.Blocks() as demo: + gr.Markdown(_HEADER_) + + with gr.Row(): + with gr.Column(): + prompt = gr.Textbox(label="Prompt", value="portrait, color, cinematic") + id_image = gr.Image(label="ID Image") + id_weight = gr.Slider(0.0, 3.0, 1, step=0.05, label="id weight") + + width = gr.Slider(256, 1536, 896, step=16, label="Width") + height = gr.Slider(256, 1536, 1152, step=16, label="Height") + num_steps = gr.Slider(1, 20, 20, step=1, label="Number of steps") + start_step = gr.Slider(0, 10, 0, step=1, label="timestep to start inserting ID") + guidance = gr.Slider(1.0, 10.0, 4, step=0.1, label="Guidance") + seed = gr.Textbox(-1, label="Seed (-1 for random)") + max_sequence_length = gr.Slider(128, 512, 128, step=128, + label="max_sequence_length for prompt (T5), small will be faster") + + with gr.Accordion("Advanced Options (True CFG, true_cfg_scale=1 means use fake CFG, >1 means use true CFG, if using true CFG, we recommend set the guidance scale to 1)", open=False): # noqa E501 + neg_prompt = gr.Textbox( + label="Negative Prompt", + value="bad quality, worst quality, text, signature, watermark, extra limbs") + true_cfg = gr.Slider(1.0, 10.0, 1, step=0.1, label="true CFG scale") + timestep_to_start_cfg = gr.Slider(0, 20, 1, step=1, label="timestep to start cfg", visible=args.dev) + + generate_btn = gr.Button("Generate") + + with gr.Column(): + output_image = gr.Image(label="Generated Image") + seed_output = gr.Textbox(label="Used Seed") + intermediate_output = gr.Gallery(label='Output', elem_id="gallery", visible=args.dev) + gr.Markdown(_CITE_) + + with gr.Row(), gr.Column(): + gr.Markdown("## Examples") + example_inps = [ + [ + 'a woman holding sign with glowing green text \"PuLID for FLUX\"', + 'example_inputs/liuyifei.png', + 4, 4, 2680261499100305976, 1 + ], + [ + 'portrait, side view', + 'example_inputs/liuyifei.png', + 4, 4, 1205240166692517553, 1 + ], + [ + 'white-haired woman with vr technology atmosphere, revolutionary exceptional magnum with remarkable details', # noqa E501 + 'example_inputs/liuyifei.png', + 4, 4, 6349424134217931066, 1 + ], + [ + 'a young child is eating Icecream', + 'example_inputs/liuyifei.png', + 4, 4, 10606046113565776207, 1 + ], + [ + 'a man is holding a sign with text \"PuLID for FLUX\", winter, snowing, top of the mountain', + 'example_inputs/pengwei.jpg', + 4, 4, 2410129802683836089, 1 + ], + [ + 'portrait, candle light', + 'example_inputs/pengwei.jpg', + 4, 4, 17522759474323955700, 1 + ], + [ + 'profile shot dark photo of a 25-year-old male with smoke escaping from his mouth, the backlit smoke gives the image an ephemeral quality, natural face, natural eyebrows, natural skin texture, award winning photo, highly detailed face, atmospheric lighting, film grain, monochrome', # noqa E501 + 'example_inputs/pengwei.jpg', + 4, 4, 17733156847328193625, 1 + ], + [ + 'American Comics, 1boy', + 'example_inputs/pengwei.jpg', + 1, 4, 13223174453874179686, 1 + ], + [ + 'portrait, pixar', + 'example_inputs/pengwei.jpg', + 1, 4, 9445036702517583939, 1 + ], + ] + gr.Examples(examples=example_inps, inputs=[prompt, id_image, start_step, guidance, seed, true_cfg], + label='fake CFG') + + example_inps = [ + [ + 'portrait, made of ice sculpture', + 'example_inputs/lecun.jpg', + 1, 1, 3811899118709451814, 5 + ], + ] + gr.Examples(examples=example_inps, inputs=[prompt, id_image, start_step, guidance, seed, true_cfg], + label='true CFG') + + generate_btn.click( + fn=generator.generate_image, + inputs=[width, height, num_steps, start_step, guidance, seed, prompt, id_image, id_weight, neg_prompt, + true_cfg, timestep_to_start_cfg, max_sequence_length], + outputs=[output_image, seed_output, intermediate_output], + ) + + return demo + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="PuLID for FLUX.1-dev") + parser.add_argument("--name", type=str, default="flux-dev", choices=list('flux-dev'), + help="currently only support flux-dev") + parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", + help="Device to use") + parser.add_argument("--offload", action="store_true", help="Offload model to CPU when not in use") + parser.add_argument("--port", type=int, default=8080, help="Port to use") + parser.add_argument("--dev", action='store_true', help="Development mode") + parser.add_argument("--pretrained_model", type=str, help='for development') + args = parser.parse_args() + + demo = create_demo(args, args.name, args.device, args.offload) + demo.launch(server_name='0.0.0.0', server_port=args.port) diff --git a/docs/pulid_for_flux.md b/docs/pulid_for_flux.md new file mode 100644 index 0000000..94bdb63 --- /dev/null +++ b/docs/pulid_for_flux.md @@ -0,0 +1,46 @@ +# PuLID for FLUX +We are happy to release the **PuLID-FLUX-v0.9.0** model, which provides a tuning-free ID customization solution for FLUX.1-dev. + +If PuLID-FLUX is helpful, please help to ⭐ this repo or recommend it to your friends 😊 + +## Inference +### Local Gradio Demo +1. Please first follow the [dependencies-and-installation](../README.md#wrench-dependencies-and-installation) to set up the environment. +2. Download flux1-dev.safetensors and ae.safetensors from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/tree/main) to the models folder. +3. Run the gradio demo with `python app_flux.py` + +### Online Demo +- huggingface demo: we are currently preparing it, will be available soon. + +### ComfyUI +Please stay tuned for the community implementation + +## Visual Results +![pulid_flux_results](https://github.com/user-attachments/assets/7eafb90a-fdd1-4ae7-bc41-8c428d568848) + + +## Useful Tips +There are two parameters that are crucial and need to be set carefully: + +1. `timestep to start inserting ID`: This parameter controls the timing of ID insertion. If set to 0, the ID starts being inserted to the DIT from the first timestep. The earlier it is inserted, the higher the ID fidelity will be, but the editability may decrease. The later it is inserted, the lower the fidelity to the ID, but the editability will increase, and the disruption to the original model behavior will also be smaller. For generating realistic images, we suggest setting this to 4. If you found the ID similarity is not high enough, you could try lowering this parameter accordingly. For generating stylized images, we suggest setting it to 0-1. +![start_id](https://github.com/user-attachments/assets/3866ffab-542d-4e2f-9a0c-6877c9158d49) + +2. `true CFG scale`: FLUX.1-dev is a guidance distill model. The original CFG process, which required twice the number of inference steps, is distilled into a guidance scale, thereby modulating the DIT through the guidance scale to simulate the true CFG process with half the inference steps. We will refer to this as fake CFG in the following doc. Our PuLID-FLUX model can be tested under the fake CFG settings, and the guidance scale can be set to a commonly used value, such as 4. However, the model also supports using the real CFG for inference. We compare the results of using true CFG with the fake CFG in photorealistic scenarios below. +![fake_cfg_vs_true_cfg_fidelity](https://github.com/user-attachments/assets/73b44dc8-37c7-48c8-8f55-73882731126d) +As shown in the above image, in terms of ID fidelity, using fake CFG is similar to true CFG in most cases, except that in a few cases, true CFG achieves higher ID similarity. In terms of image aesthetics and facial naturalness, fake CFG performs better. However, by carefully adjusting hyperparameters, the performance of true CFG may be further improved, we leave this to the community to explore. Therefore, we recommend using fake CFG for photorealistic scenes. If you are not satisfy about the ID fidelity, you can try switching to true CFG. Additionally, as shown below, we have found that using fake CFG in stylized scenes sometimes results in lower ID similarity and poorer style response, so if you encounter these two issues in stylized scenes, please consider switching to true CFG. +![fake_cfg_vs_true_cfg_style](https://github.com/user-attachments/assets/fb042639-64e6-4bb3-a3a4-5c138793318e) + + + +## Some Technical Details +- We switch the ID encoder from an MLP structure to a Transformer structure. Interested users can refer to [source code](../pulid/encoders_flux.py) +- Inspired by [Flamingo](https://arxiv.org/abs/2204.14198), we insert additional cross-attention blocks every few DIT blocks to interact ID features with DIT image features +- We would like to clarify that the acceleration method (lile SDXL-Lightning) serves as an +optional acceleration trick, but it is not indispensable for training PuLID. We will update the arxiv paper with the relevant details in the near future. Please stay tuned. + + +## limitation +The model is currently in beta version, and we have observed that the ID fidelity may not be high for some male inputs, maybe the model requires more training. If the improved model is ready, we will release it here, so please stay tuned. + +## contact +If you have any questions or suggestions about the model, please contact [Yanze Wu](https://tothebeginning.github.io/) or open an issue/discussion here. \ No newline at end of file diff --git a/v1.1_preview.md b/docs/v1.1_preview.md similarity index 100% rename from v1.1_preview.md rename to docs/v1.1_preview.md diff --git a/example_inputs/pengwei.jpg b/example_inputs/pengwei.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4cadc20ed63b7070ce57ec904177f1b1dd7918f3 GIT binary patch literal 1244849 zcmdSBby!s2_b7a3h6V{01nE#px`qysQo36N!~yB<20=Qcqy(fH5s;FO0aQ?0x{>aN z;U4|O@B7~8zW2Gm_n-H<>(n}Xuf6tKd+k`~oHcVjemw^eE6FL!0T2iPD1aMqJJRt0pb0F-v;q&=&k?a6OI5d{^rvIR?aQ$;PBECK&ZX2x3i?<;h|G= zvZqrv^8iVHOZ&U#zpgxdoI(J=%gxIp!Yw4iBS^<1EW!;J;pYW_dqL^{$ODZG%KR5k zc*XEvvb|#d3x`MnKpX&IgM2Z#d3kx_VE@PoO^?I)7q5u>s~tc(XnP#VpY-2&U);@q 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za#&d;z?+0aAi@&aVV_VdE*MZ)lq}4|{M`r`J$*7U#Q;@`7l|32RG~RO+;qi8a_F(c zl)(%JIV5)cYVFDh6&?vxjBaH)+TN-4^jPUDH<6pqn5~s_Wk0(Ghhy*IOn(0 zq*<5DV90l}oDc^d-t@X^pt_pIRSy~ht)8fJoS$F9fUukG<%i~dqXTlBo*R#p41T7X zXMzh!?d@|cxWNQqV?40yh8;2b){IKJgYT6B!*7_^pw8ZS_db01L{)7$Z18k9wZpN%Bz49PJ$Z#Fjm=p17%W)`rz2ic)59 z#hsT2ApV&jrA>9HgpEu=erD%)b)g9(fbk`?a-D(6lqmGuf%NHI&8q#Gp4sqOfx#U0 z2fuH6-Pf6JUNaMUVgN_Y-=A(s70tUz3;F7&<;LQD{VG~*+#U87*^|ss8*=e`-TGUTiFY$CZV_FpNIcNPV`Bk^ z?hZ)E>F>o!ocazQx-e~4{4r4mZYRf>qFBLMZvjOWmU-lui9Xbh4C-MFDV zoNXEHP?!`oFtT}f4gR7M89+Za;&I2Q&p}$gb4*p^CDuT|16Y^hC52>>e8z(bgVX9q zr7f-a%7jxWy=M~`8Ra}tH(Sg71NN!?oosDW3RFIqs&d}T>Dgz*o2SwgcH3- zB|UoJ=h~H*?7%&%tiu?6+zfYMdBNuur#-crS??@d6<&HbK(PzTP3xYAvQPYkOJ?WZ+5KVRSrm}ULHLD;1ADHA5 s*Wcc^{?!90#Gtb;Bys@n^fie3f17% str: + return self.__repr__() + + def __repr__(self) -> str: + if self.detail is None: + message = None + elif isinstance(self.detail, str): + message = self.detail + else: + message = "[" + ",".join(d["msg"] for d in self.detail) + "]" + return f"ApiException({self.status_code=}, {message=}, detail={self.detail})" + + +class ImageRequest: + def __init__( + self, + prompt: str, + width: int = 1024, + height: int = 1024, + name: str = "flux.1-pro", + num_steps: int = 50, + prompt_upsampling: bool = False, + seed: int = None, + validate: bool = True, + launch: bool = True, + api_key: str = None, + ): + """ + Manages an image generation request to the API. + + Args: + prompt: Prompt to sample + width: Width of the image in pixel + height: Height of the image in pixel + name: Name of the model + num_steps: Number of network evaluations + prompt_upsampling: Use prompt upsampling + seed: Fix the generation seed + validate: Run input validation + launch: Directly launches request + api_key: Your API key if not provided by the environment + + Raises: + ValueError: For invalid input + ApiException: For errors raised from the API + """ + if validate: + if name not in ["flux.1-pro"]: + raise ValueError(f"Invalid model {name}") + elif width % 32 != 0: + raise ValueError(f"width must be divisible by 32, got {width}") + elif not (256 <= width <= 1440): + raise ValueError(f"width must be between 256 and 1440, got {width}") + elif height % 32 != 0: + raise ValueError(f"height must be divisible by 32, got {height}") + elif not (256 <= height <= 1440): + raise ValueError(f"height must be between 256 and 1440, got {height}") + elif not (1 <= num_steps <= 50): + raise ValueError(f"steps must be between 1 and 50, got {num_steps}") + + self.request_json = { + "prompt": prompt, + "width": width, + "height": height, + "variant": name, + "steps": num_steps, + "prompt_upsampling": prompt_upsampling, + } + if seed is not None: + self.request_json["seed"] = seed + + self.request_id: str = None + self.result: dict = None + self._image_bytes: bytes = None + self._url: str = None + if api_key is None: + self.api_key = os.environ.get("BFL_API_KEY") + else: + self.api_key = api_key + + if launch: + self.request() + + def request(self): + """ + Request to generate the image. + """ + if self.request_id is not None: + return + response = requests.post( + f"{API_ENDPOINT}/v1/image", + headers={ + "accept": "application/json", + "x-key": self.api_key, + "Content-Type": "application/json", + }, + json=self.request_json, + ) + result = response.json() + if response.status_code != 200: + raise ApiException(status_code=response.status_code, detail=result.get("detail")) + self.request_id = response.json()["id"] + + def retrieve(self) -> dict: + """ + Wait for the generation to finish and retrieve response. + """ + if self.request_id is None: + self.request() + while self.result is None: + response = requests.get( + f"{API_ENDPOINT}/v1/get_result", + headers={ + "accept": "application/json", + "x-key": self.api_key, + }, + params={ + "id": self.request_id, + }, + ) + result = response.json() + if "status" not in result: + raise ApiException(status_code=response.status_code, detail=result.get("detail")) + elif result["status"] == "Ready": + self.result = result["result"] + elif result["status"] == "Pending": + time.sleep(0.5) + else: + raise ApiException(status_code=200, detail=f"API returned status '{result['status']}'") + return self.result + + @property + def bytes(self) -> bytes: + """ + Generated image as bytes. + """ + if self._image_bytes is None: + response = requests.get(self.url) + if response.status_code == 200: + self._image_bytes = response.content + else: + raise ApiException(status_code=response.status_code) + return self._image_bytes + + @property + def url(self) -> str: + """ + Public url to retrieve the image from + """ + if self._url is None: + result = self.retrieve() + self._url = result["sample"] + return self._url + + @property + def image(self) -> Image.Image: + """ + Load the image as a PIL Image + """ + return Image.open(io.BytesIO(self.bytes)) + + def save(self, path: str): + """ + Save the generated image to a local path + """ + suffix = Path(self.url).suffix + if not path.endswith(suffix): + path = path + suffix + Path(path).resolve().parent.mkdir(parents=True, exist_ok=True) + with open(path, "wb") as file: + file.write(self.bytes) + + +if __name__ == "__main__": + from fire import Fire + + Fire(ImageRequest) diff --git a/flux/cli.py b/flux/cli.py new file mode 100644 index 0000000..8333963 --- /dev/null +++ b/flux/cli.py @@ -0,0 +1,261 @@ +import os +import re +import time +from dataclasses import dataclass +from glob import iglob + +import torch +from einops import rearrange +from fire import Fire +from PIL import ExifTags, Image +from transformers import pipeline + +from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack +from flux.util import ( + configs, + embed_watermark, + load_ae, + load_clip, + load_flow_model, + load_t5, +) + +NSFW_THRESHOLD = 0.85 + + +@dataclass +class SamplingOptions: + prompt: str + width: int + height: int + num_steps: int + guidance: float + seed: int + + +def parse_prompt(options: SamplingOptions) -> SamplingOptions: + user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n" + usage = ( + "Usage: Either write your prompt directly, leave this field empty " + "to repeat the prompt or write a command starting with a slash:\n" + "- '/w ' will set the width of the generated image\n" + "- '/h ' will set the height of the generated image\n" + "- '/s ' sets the next seed\n" + "- '/g ' sets the guidance (flux-dev only)\n" + "- '/n ' sets the number of steps\n" + "- '/q' to quit" + ) + + while (prompt := input(user_question)).startswith("/"): + if prompt.startswith("/w"): + if prompt.count(" ") != 1: + print(f"Got invalid command '{prompt}'\n{usage}") + continue + _, width = prompt.split() + options.width = 16 * (int(width) // 16) + print( + f"Setting resolution to {options.width} x {options.height} " + f"({options.height * options.width / 1e6:.2f}MP)" + ) + elif prompt.startswith("/h"): + if prompt.count(" ") != 1: + print(f"Got invalid command '{prompt}'\n{usage}") + continue + _, height = prompt.split() + options.height = 16 * (int(height) // 16) + print( + f"Setting resolution to {options.width} x {options.height} " + f"({options.height * options.width / 1e6:.2f}MP)" + ) + elif prompt.startswith("/g"): + if prompt.count(" ") != 1: + print(f"Got invalid command '{prompt}'\n{usage}") + continue + _, guidance = prompt.split() + options.guidance = float(guidance) + print(f"Setting guidance to {options.guidance}") + elif prompt.startswith("/s"): + if prompt.count(" ") != 1: + print(f"Got invalid command '{prompt}'\n{usage}") + continue + _, seed = prompt.split() + options.seed = int(seed) + print(f"Setting seed to {options.seed}") + elif prompt.startswith("/n"): + if prompt.count(" ") != 1: + print(f"Got invalid command '{prompt}'\n{usage}") + continue + _, steps = prompt.split() + options.num_steps = int(steps) + print(f"Setting seed to {options.num_steps}") + elif prompt.startswith("/q"): + print("Quitting") + return None + else: + if not prompt.startswith("/h"): + print(f"Got invalid command '{prompt}'\n{usage}") + print(usage) + if prompt != "": + options.prompt = prompt + return options + + +@torch.inference_mode() +def main( + name: str = "flux-schnell", + width: int = 1360, + height: int = 768, + seed: int = None, + prompt: str = ( + "a photo of a forest with mist swirling around the tree trunks. The word " + '"FLUX" is painted over it in big, red brush strokes with visible texture' + ), + device: str = "cuda" if torch.cuda.is_available() else "cpu", + num_steps: int = None, + loop: bool = False, + guidance: float = 3.5, + offload: bool = False, + output_dir: str = "output", + add_sampling_metadata: bool = True, +): + """ + Sample the flux model. Either interactively (set `--loop`) or run for a + single image. + + Args: + name: Name of the model to load + height: height of the sample in pixels (should be a multiple of 16) + width: width of the sample in pixels (should be a multiple of 16) + seed: Set a seed for sampling + output_name: where to save the output image, `{idx}` will be replaced + by the index of the sample + prompt: Prompt used for sampling + device: Pytorch device + num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled) + loop: start an interactive session and sample multiple times + guidance: guidance value used for guidance distillation + add_sampling_metadata: Add the prompt to the image Exif metadata + """ + nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection") + + if name not in configs: + available = ", ".join(configs.keys()) + raise ValueError(f"Got unknown model name: {name}, chose from {available}") + + torch_device = torch.device(device) + if num_steps is None: + num_steps = 4 if name == "flux-schnell" else 50 + + # allow for packing and conversion to latent space + height = 16 * (height // 16) + width = 16 * (width // 16) + + output_name = os.path.join(output_dir, "img_{idx}.jpg") + if not os.path.exists(output_dir): + os.makedirs(output_dir) + idx = 0 + else: + fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]\.jpg$", fn)] + if len(fns) > 0: + idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1 + else: + idx = 0 + + # init all components + t5 = load_t5(torch_device, max_length=256 if name == "flux-schnell" else 512) + clip = load_clip(torch_device) + model = load_flow_model(name, device="cpu" if offload else torch_device) + ae = load_ae(name, device="cpu" if offload else torch_device) + + rng = torch.Generator(device="cpu") + opts = SamplingOptions( + prompt=prompt, + width=width, + height=height, + num_steps=num_steps, + guidance=guidance, + seed=seed, + ) + + if loop: + opts = parse_prompt(opts) + + while opts is not None: + if opts.seed is None: + opts.seed = rng.seed() + print(f"Generating with seed {opts.seed}:\n{opts.prompt}") + t0 = time.perf_counter() + + # prepare input + x = get_noise( + 1, + opts.height, + opts.width, + device=torch_device, + dtype=torch.bfloat16, + seed=opts.seed, + ) + opts.seed = None + if offload: + ae = ae.cpu() + torch.cuda.empty_cache() + t5, clip = t5.to(torch_device), clip.to(torch_device) + inp = prepare(t5, clip, x, prompt=opts.prompt) + timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell")) + + # offload TEs to CPU, load model to gpu + if offload: + t5, clip = t5.cpu(), clip.cpu() + torch.cuda.empty_cache() + model = model.to(torch_device) + + # denoise initial noise + x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance) + + # offload model, load autoencoder to gpu + if offload: + model.cpu() + torch.cuda.empty_cache() + ae.decoder.to(x.device) + + # decode latents to pixel space + x = unpack(x.float(), opts.height, opts.width) + with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16): + x = ae.decode(x) + t1 = time.perf_counter() + + fn = output_name.format(idx=idx) + print(f"Done in {t1 - t0:.1f}s. Saving {fn}") + # bring into PIL format and save + x = x.clamp(-1, 1) + x = embed_watermark(x.float()) + x = rearrange(x[0], "c h w -> h w c") + + img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy()) + nsfw_score = [x["score"] for x in nsfw_classifier(img) if x["label"] == "nsfw"][0] + + if nsfw_score < NSFW_THRESHOLD: + exif_data = Image.Exif() + exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux" + exif_data[ExifTags.Base.Make] = "Black Forest Labs" + exif_data[ExifTags.Base.Model] = name + if add_sampling_metadata: + exif_data[ExifTags.Base.ImageDescription] = prompt + img.save(fn, exif=exif_data, quality=95, subsampling=0) + idx += 1 + else: + print("Your generated image may contain NSFW content.") + + if loop: + print("-" * 80) + opts = parse_prompt(opts) + else: + opts = None + + +def app(): + Fire(main) + + +if __name__ == "__main__": + app() diff --git a/flux/math.py b/flux/math.py new file mode 100644 index 0000000..6c76bdc --- /dev/null +++ b/flux/math.py @@ -0,0 +1,31 @@ +import torch +from einops import rearrange +from torch import Tensor + + +def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor: + if pe is not None: + q, k = apply_rope(q, k, pe) + + x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + x = rearrange(x, "B H L D -> B L (H D)") + + return x + + +def rope(pos: Tensor, dim: int, theta: int) -> Tensor: + assert dim % 2 == 0 + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos, omega) + out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1) + out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2) + return out.float() + + +def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]: + xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) + xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) + xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] + xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] + return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) diff --git a/flux/model.py b/flux/model.py new file mode 100644 index 0000000..619a466 --- /dev/null +++ b/flux/model.py @@ -0,0 +1,135 @@ +from dataclasses import dataclass + +import torch +from torch import Tensor, nn + +from flux.modules.layers import ( + DoubleStreamBlock, + EmbedND, + LastLayer, + MLPEmbedder, + SingleStreamBlock, + timestep_embedding, +) + + +@dataclass +class FluxParams: + in_channels: int + vec_in_dim: int + context_in_dim: int + hidden_size: int + mlp_ratio: float + num_heads: int + depth: int + depth_single_blocks: int + axes_dim: list[int] + theta: int + qkv_bias: bool + guidance_embed: bool + + +class Flux(nn.Module): + """ + Transformer model for flow matching on sequences. + """ + + def __init__(self, params: FluxParams): + super().__init__() + + self.params = params + self.in_channels = params.in_channels + self.out_channels = self.in_channels + if params.hidden_size % params.num_heads != 0: + raise ValueError( + f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}" + ) + pe_dim = params.hidden_size // params.num_heads + if sum(params.axes_dim) != pe_dim: + raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}") + self.hidden_size = params.hidden_size + self.num_heads = params.num_heads + self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim) + self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) + self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) + self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size) + self.guidance_in = ( + MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity() + ) + self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size) + + self.double_blocks = nn.ModuleList( + [ + DoubleStreamBlock( + self.hidden_size, + self.num_heads, + mlp_ratio=params.mlp_ratio, + qkv_bias=params.qkv_bias, + ) + for _ in range(params.depth) + ] + ) + + self.single_blocks = nn.ModuleList( + [ + SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) + for _ in range(params.depth_single_blocks) + ] + ) + + self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) + + self.pulid_ca = None + self.pulid_double_interval = 2 + self.pulid_single_interval = 4 + + def forward( + self, + img: Tensor, + img_ids: Tensor, + txt: Tensor, + txt_ids: Tensor, + timesteps: Tensor, + y: Tensor, + guidance: Tensor = None, + id: Tensor = None, + id_weight: float = 1.0, + ) -> Tensor: + if img.ndim != 3 or txt.ndim != 3: + raise ValueError("Input img and txt tensors must have 3 dimensions.") + + # running on sequences img + img = self.img_in(img) + vec = self.time_in(timestep_embedding(timesteps, 256)) + if self.params.guidance_embed: + if guidance is None: + raise ValueError("Didn't get guidance strength for guidance distilled model.") + vec = vec + self.guidance_in(timestep_embedding(guidance, 256)) + vec = vec + self.vector_in(y) + txt = self.txt_in(txt) + + ids = torch.cat((txt_ids, img_ids), dim=1) + pe = self.pe_embedder(ids) + + ca_idx = 0 + for i, block in enumerate(self.double_blocks): + img, txt = block(img=img, txt=txt, vec=vec, pe=pe) + + if i % self.pulid_double_interval == 0 and id is not None: + img = img + id_weight * self.pulid_ca[ca_idx](id, img) + ca_idx += 1 + + img = torch.cat((txt, img), 1) + for i, block in enumerate(self.single_blocks): + x = block(img, vec=vec, pe=pe) + real_img, txt = x[:, txt.shape[1]:, ...], x[:, :txt.shape[1], ...] + + if i % self.pulid_single_interval == 0 and id is not None: + real_img = real_img + id_weight * self.pulid_ca[ca_idx](id, real_img) + ca_idx += 1 + + img = torch.cat((txt, real_img), 1) + img = img[:, txt.shape[1] :, ...] + + img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels) + return img diff --git a/flux/modules/__init__.py b/flux/modules/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/flux/modules/autoencoder.py b/flux/modules/autoencoder.py new file mode 100644 index 0000000..75159f7 --- /dev/null +++ b/flux/modules/autoencoder.py @@ -0,0 +1,312 @@ +from dataclasses import dataclass + +import torch +from einops import rearrange +from torch import Tensor, nn + + +@dataclass +class AutoEncoderParams: + resolution: int + in_channels: int + ch: int + out_ch: int + ch_mult: list[int] + num_res_blocks: int + z_channels: int + scale_factor: float + shift_factor: float + + +def swish(x: Tensor) -> Tensor: + return x * torch.sigmoid(x) + + +class AttnBlock(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.in_channels = in_channels + + self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + + self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1) + self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1) + + def attention(self, h_: Tensor) -> Tensor: + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + b, c, h, w = q.shape + q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous() + k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous() + v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous() + h_ = nn.functional.scaled_dot_product_attention(q, k, v) + + return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b) + + def forward(self, x: Tensor) -> Tensor: + return x + self.proj_out(self.attention(x)) + + +class ResnetBlock(nn.Module): + def __init__(self, in_channels: int, out_channels: int): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + + self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + if self.in_channels != self.out_channels: + self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + h = x + h = self.norm1(h) + h = swish(h) + h = self.conv1(h) + + h = self.norm2(h) + h = swish(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + x = self.nin_shortcut(x) + + return x + h + + +class Downsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + # no asymmetric padding in torch conv, must do it ourselves + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + + def forward(self, x: Tensor): + pad = (0, 1, 0, 1) + x = nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels: int): + super().__init__() + self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor): + x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + return x + + +class Encoder(nn.Module): + def __init__( + self, + resolution: int, + in_channels: int, + ch: int, + ch_mult: list[int], + num_res_blocks: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + # downsampling + self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + block_in = self.ch + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1) + + def forward(self, x: Tensor) -> Tensor: + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1]) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions - 1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__( + self, + ch: int, + out_ch: int, + ch_mult: list[int], + num_res_blocks: int, + in_channels: int, + resolution: int, + z_channels: int, + ): + super().__init__() + self.ch = ch + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.ffactor = 2 ** (self.num_resolutions - 1) + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + + # z to block_in + self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for _ in range(self.num_res_blocks + 1): + block.append(ResnetBlock(in_channels=block_in, out_channels=block_out)) + block_in = block_out + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) + self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1) + + def forward(self, z: Tensor) -> Tensor: + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h) + h = self.mid.attn_1(h) + h = self.mid.block_2(h) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = swish(h) + h = self.conv_out(h) + return h + + +class DiagonalGaussian(nn.Module): + def __init__(self, sample: bool = True, chunk_dim: int = 1): + super().__init__() + self.sample = sample + self.chunk_dim = chunk_dim + + def forward(self, z: Tensor) -> Tensor: + mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim) + if self.sample: + std = torch.exp(0.5 * logvar) + return mean + std * torch.randn_like(mean) + else: + return mean + + +class AutoEncoder(nn.Module): + def __init__(self, params: AutoEncoderParams): + super().__init__() + self.encoder = Encoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.decoder = Decoder( + resolution=params.resolution, + in_channels=params.in_channels, + ch=params.ch, + out_ch=params.out_ch, + ch_mult=params.ch_mult, + num_res_blocks=params.num_res_blocks, + z_channels=params.z_channels, + ) + self.reg = DiagonalGaussian() + + self.scale_factor = params.scale_factor + self.shift_factor = params.shift_factor + + def encode(self, x: Tensor) -> Tensor: + z = self.reg(self.encoder(x)) + z = self.scale_factor * (z - self.shift_factor) + return z + + def decode(self, z: Tensor) -> Tensor: + z = z / self.scale_factor + self.shift_factor + return self.decoder(z) + + def forward(self, x: Tensor) -> Tensor: + return self.decode(self.encode(x)) diff --git a/flux/modules/conditioner.py b/flux/modules/conditioner.py new file mode 100644 index 0000000..e60297e --- /dev/null +++ b/flux/modules/conditioner.py @@ -0,0 +1,37 @@ +from torch import Tensor, nn +from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer + + +class HFEmbedder(nn.Module): + def __init__(self, version: str, max_length: int, **hf_kwargs): + super().__init__() + self.is_clip = version.startswith("openai") + self.max_length = max_length + self.output_key = "pooler_output" if self.is_clip else "last_hidden_state" + + if self.is_clip: + self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length) + self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs) + else: + self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length) + self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs) + + self.hf_module = self.hf_module.eval().requires_grad_(False) + + def forward(self, text: list[str]) -> Tensor: + batch_encoding = self.tokenizer( + text, + truncation=True, + max_length=self.max_length, + return_length=False, + return_overflowing_tokens=False, + padding="max_length", + return_tensors="pt", + ) + + outputs = self.hf_module( + input_ids=batch_encoding["input_ids"].to(self.hf_module.device), + attention_mask=None, + output_hidden_states=False, + ) + return outputs[self.output_key] diff --git a/flux/modules/layers.py b/flux/modules/layers.py new file mode 100644 index 0000000..fc36b05 --- /dev/null +++ b/flux/modules/layers.py @@ -0,0 +1,253 @@ +import math +from dataclasses import dataclass + +import torch +from einops import rearrange +from torch import Tensor, nn + +from flux.math import attention, rope + + +class EmbedND(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: list[int]): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = axes_dim + + def forward(self, ids: Tensor) -> Tensor: + n_axes = ids.shape[-1] + emb = torch.cat( + [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], + dim=-3, + ) + + return emb.unsqueeze(1) + + +def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + t = time_factor * t + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( + t.device + ) + + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + if torch.is_floating_point(t): + embedding = embedding.to(t) + return embedding + + +class MLPEmbedder(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int): + super().__init__() + self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) + self.silu = nn.SiLU() + self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) + + def forward(self, x: Tensor) -> Tensor: + return self.out_layer(self.silu(self.in_layer(x))) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.scale = nn.Parameter(torch.ones(dim)) + + def forward(self, x: Tensor): + x_dtype = x.dtype + x = x.float() + rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6) + return (x * rrms).to(dtype=x_dtype) * self.scale + + +class QKNorm(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + self.query_norm = RMSNorm(dim) + self.key_norm = RMSNorm(dim) + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]: + q = self.query_norm(q) + k = self.key_norm(k) + return q.to(v), k.to(v) + + +class SelfAttention(nn.Module): + def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.norm = QKNorm(head_dim) + self.proj = nn.Linear(dim, dim) + + def forward(self, x: Tensor, pe: Tensor) -> Tensor: + qkv = self.qkv(x) + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + x = attention(q, k, v, pe=pe) + x = self.proj(x) + return x + + +@dataclass +class ModulationOut: + shift: Tensor + scale: Tensor + gate: Tensor + + +class Modulation(nn.Module): + def __init__(self, dim: int, double: bool): + super().__init__() + self.is_double = double + self.multiplier = 6 if double else 3 + self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) + + def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut]: + out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1) + + return ( + ModulationOut(*out[:3]), + ModulationOut(*out[3:]) if self.is_double else None, + ) + + +class DoubleStreamBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False): + super().__init__() + + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.num_heads = num_heads + self.hidden_size = hidden_size + self.img_mod = Modulation(hidden_size, double=True) + self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.img_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + self.txt_mod = Modulation(hidden_size, double=True) + self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) + + self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.txt_mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden_dim, bias=True), + nn.GELU(approximate="tanh"), + nn.Linear(mlp_hidden_dim, hidden_size, bias=True), + ) + + def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]: + img_mod1, img_mod2 = self.img_mod(vec) + txt_mod1, txt_mod2 = self.txt_mod(vec) + + # prepare image for attention + img_modulated = self.img_norm1(img) + img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift + img_qkv = self.img_attn.qkv(img_modulated) + img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) + + # prepare txt for attention + txt_modulated = self.txt_norm1(txt) + txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift + txt_qkv = self.txt_attn.qkv(txt_modulated) + txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) + + # run actual attention + q = torch.cat((txt_q, img_q), dim=2) + k = torch.cat((txt_k, img_k), dim=2) + v = torch.cat((txt_v, img_v), dim=2) + + attn = attention(q, k, v, pe=pe) + txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :] + + # calculate the img bloks + img = img + img_mod1.gate * self.img_attn.proj(img_attn) + img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) + + # calculate the txt bloks + txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) + txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) + return img, txt + + +class SingleStreamBlock(nn.Module): + """ + A DiT block with parallel linear layers as described in + https://arxiv.org/abs/2302.05442 and adapted modulation interface. + """ + + def __init__( + self, + hidden_size: int, + num_heads: int, + mlp_ratio: float = 4.0, + qk_scale: float = None, + ): + super().__init__() + self.hidden_dim = hidden_size + self.num_heads = num_heads + head_dim = hidden_size // num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.mlp_hidden_dim = int(hidden_size * mlp_ratio) + # qkv and mlp_in + self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) + # proj and mlp_out + self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) + + self.norm = QKNorm(head_dim) + + self.hidden_size = hidden_size + self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + + self.mlp_act = nn.GELU(approximate="tanh") + self.modulation = Modulation(hidden_size, double=False) + + def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor: + mod, _ = self.modulation(vec) + x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift + qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) + + q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) + q, k = self.norm(q, k, v) + + # compute attention + attn = attention(q, k, v, pe=pe) + # compute activation in mlp stream, cat again and run second linear layer + output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) + return x + mod.gate * output + + +class LastLayer(nn.Module): + def __init__(self, hidden_size: int, patch_size: int, out_channels: int): + super().__init__() + self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) + + def forward(self, x: Tensor, vec: Tensor) -> Tensor: + shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) + x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] + x = self.linear(x) + return x diff --git a/flux/sampling.py b/flux/sampling.py new file mode 100644 index 0000000..9b1866e --- /dev/null +++ b/flux/sampling.py @@ -0,0 +1,161 @@ +import math +from typing import Callable + +import torch +from einops import rearrange, repeat +from torch import Tensor + +from .model import Flux +from .modules.conditioner import HFEmbedder + + +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: HFEmbedder, clip: HFEmbedder, img: Tensor, prompt: 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, + timesteps: list[float], + guidance: float = 4.0, + id_weight=1.0, + id=None, + start_step=0, + uncond_id=None, + true_cfg=1.0, + timestep_to_start_cfg=1, + neg_txt=None, + neg_txt_ids=None, + neg_vec=None, +): + # this is ignored for schnell + guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype) + use_true_cfg = abs(true_cfg - 1.0) > 1e-2 + for i, (t_curr, t_prev) in enumerate(zip(timesteps[:-1], timesteps[1:])): + 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, + id=id if i >= start_step else None, + id_weight=id_weight, + ) + + if use_true_cfg and i >= timestep_to_start_cfg: + neg_pred = model( + img=img, + img_ids=img_ids, + txt=neg_txt, + txt_ids=neg_txt_ids, + y=neg_vec, + timesteps=t_vec, + guidance=guidance_vec, + id=uncond_id if i >= start_step else None, + id_weight=id_weight, + ) + pred = neg_pred + true_cfg * (pred - neg_pred) + + 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, + ) diff --git a/flux/util.py b/flux/util.py new file mode 100644 index 0000000..011b306 --- /dev/null +++ b/flux/util.py @@ -0,0 +1,201 @@ +import os +from dataclasses import dataclass + +import torch +from einops import rearrange +from huggingface_hub import hf_hub_download +from imwatermark import WatermarkEncoder +from safetensors.torch import load_file as load_sft + +from flux.model import Flux, FluxParams +from flux.modules.autoencoder import AutoEncoder, AutoEncoderParams +from flux.modules.conditioner import HFEmbedder + + +@dataclass +class ModelSpec: + params: FluxParams + ae_params: AutoEncoderParams + ckpt_path: str + ae_path: str + repo_id: str + repo_flow: str + repo_ae: str + + +configs = { + "flux-dev": ModelSpec( + repo_id="black-forest-labs/FLUX.1-dev", + repo_flow="flux1-dev.safetensors", + repo_ae="ae.safetensors", + ckpt_path='models/flux1-dev.safetensors', + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=True, + ), + ae_path='models/ae.safetensors', + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), + "flux-schnell": ModelSpec( + repo_id="black-forest-labs/FLUX.1-schnell", + repo_flow="flux1-schnell.safetensors", + repo_ae="ae.safetensors", + ckpt_path=os.getenv("FLUX_SCHNELL"), + params=FluxParams( + in_channels=64, + vec_in_dim=768, + context_in_dim=4096, + hidden_size=3072, + mlp_ratio=4.0, + num_heads=24, + depth=19, + depth_single_blocks=38, + axes_dim=[16, 56, 56], + theta=10_000, + qkv_bias=True, + guidance_embed=False, + ), + ae_path=os.getenv("AE"), + ae_params=AutoEncoderParams( + resolution=256, + in_channels=3, + ch=128, + out_ch=3, + ch_mult=[1, 2, 4, 4], + num_res_blocks=2, + z_channels=16, + scale_factor=0.3611, + shift_factor=0.1159, + ), + ), +} + + +def print_load_warning(missing: list[str], unexpected: list[str]) -> None: + if len(missing) > 0 and len(unexpected) > 0: + print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing)) + print("\n" + "-" * 79 + "\n") + print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected)) + elif len(missing) > 0: + print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing)) + elif len(unexpected) > 0: + print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected)) + + +def load_flow_model(name: str, device: str = "cuda", hf_download: bool = True): + # Loading Flux + print("Init model") + ckpt_path = configs[name].ckpt_path + if ( + ckpt_path is None + and configs[name].repo_id is not None + and configs[name].repo_flow is not None + and hf_download + ): + ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow) + + with torch.device(device): + model = Flux(configs[name].params).to(torch.bfloat16) + + if ckpt_path is not None: + print("Loading checkpoint") + # load_sft doesn't support torch.device + sd = load_sft(ckpt_path, device=str(device)) + missing, unexpected = model.load_state_dict(sd, strict=False) + print_load_warning(missing, unexpected) + return model + + +def load_t5(device: str = "cuda", max_length: int = 512) -> HFEmbedder: + # max length 64, 128, 256 and 512 should work (if your sequence is short enough) + return HFEmbedder("xlabs-ai/xflux_text_encoders", max_length=max_length, torch_dtype=torch.bfloat16).to(device) + + +def load_clip(device: str = "cuda") -> HFEmbedder: + return HFEmbedder("openai/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to(device) + + +def load_ae(name: str, device: str = "cuda", hf_download: bool = True) -> AutoEncoder: + ckpt_path = configs[name].ae_path + if ( + ckpt_path is None + and configs[name].repo_id is not None + and configs[name].repo_ae is not None + and hf_download + ): + ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_ae) + + # Loading the autoencoder + print("Init AE") + with torch.device(device): + ae = AutoEncoder(configs[name].ae_params) + + if ckpt_path is not None: + sd = load_sft(ckpt_path, device=str(device)) + missing, unexpected = ae.load_state_dict(sd, strict=False) + print_load_warning(missing, unexpected) + return ae + + +class WatermarkEmbedder: + def __init__(self, watermark): + self.watermark = watermark + self.num_bits = len(WATERMARK_BITS) + self.encoder = WatermarkEncoder() + self.encoder.set_watermark("bits", self.watermark) + + def __call__(self, image: torch.Tensor) -> torch.Tensor: + """ + Adds a predefined watermark to the input image + + Args: + image: ([N,] B, RGB, H, W) in range [-1, 1] + + Returns: + same as input but watermarked + """ + image = 0.5 * image + 0.5 + squeeze = len(image.shape) == 4 + if squeeze: + image = image[None, ...] + n = image.shape[0] + image_np = rearrange((255 * image).detach().cpu(), "n b c h w -> (n b) h w c").numpy()[:, :, :, ::-1] + # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255] + # watermarking libary expects input as cv2 BGR format + for k in range(image_np.shape[0]): + image_np[k] = self.encoder.encode(image_np[k], "dwtDct") + image = torch.from_numpy(rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)).to( + image.device + ) + image = torch.clamp(image / 255, min=0.0, max=1.0) + if squeeze: + image = image[0] + image = 2 * image - 1 + return image + + +# A fixed 48-bit message that was choosen at random +WATERMARK_MESSAGE = 0b001010101111111010000111100111001111010100101110 +# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1 +WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]] +embed_watermark = WatermarkEmbedder(WATERMARK_BITS) diff --git a/pulid/encoders_flux.py b/pulid/encoders_flux.py new file mode 100644 index 0000000..7891fb3 --- /dev/null +++ b/pulid/encoders_flux.py @@ -0,0 +1,207 @@ +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + # (bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttentionCA(nn.Module): + def __init__(self, *, dim=3072, dim_head=128, heads=16, kv_dim=2048): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + k, v = self.to_kv(x).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8, kv_dim=None): + super().__init__() + self.scale = dim_head ** -0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim if kv_dim is None else kv_dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim if kv_dim is None else kv_dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, seq_len, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, seq_len, -1) + + return self.to_out(out) + + +class IDFormer(nn.Module): + """ + - perceiver resampler like arch (compared with previous MLP-like arch) + - we concat id embedding (generated by arcface) and query tokens as latents + - latents will attend each other and interact with vit features through cross-attention + - vit features are multi-scaled and inserted into IDFormer in order, currently, each scale corresponds to two + IDFormer layers + """ + def __init__( + self, + dim=1024, + depth=10, + dim_head=64, + heads=16, + num_id_token=5, + num_queries=32, + output_dim=2048, + ff_mult=4, + ): + super().__init__() + + self.num_id_token = num_id_token + self.dim = dim + self.num_queries = num_queries + assert depth % 5 == 0 + self.depth = depth // 5 + scale = dim ** -0.5 + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) * scale) + self.proj_out = nn.Parameter(scale * torch.randn(dim, output_dim)) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + for i in range(5): + setattr( + self, + f'mapping_{i}', + nn.Sequential( + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim), + ), + ) + + self.id_embedding_mapping = nn.Sequential( + nn.Linear(1280, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, 1024), + nn.LayerNorm(1024), + nn.LeakyReLU(), + nn.Linear(1024, dim * num_id_token), + ) + + def forward(self, x, y): + + latents = self.latents.repeat(x.size(0), 1, 1) + + x = self.id_embedding_mapping(x) + x = x.reshape(-1, self.num_id_token, self.dim) + + latents = torch.cat((latents, x), dim=1) + + for i in range(5): + vit_feature = getattr(self, f'mapping_{i}')(y[i]) + ctx_feature = torch.cat((x, vit_feature), dim=1) + for attn, ff in self.layers[i * self.depth: (i + 1) * self.depth]: + latents = attn(ctx_feature, latents) + latents + latents = ff(latents) + latents + + latents = latents[:, :self.num_queries] + latents = latents @ self.proj_out + return latents diff --git a/pulid/pipeline.py b/pulid/pipeline.py index edec91c..7052151 100644 --- a/pulid/pipeline.py +++ b/pulid/pipeline.py @@ -152,7 +152,7 @@ def get_id_embedding(self, image): # get antelopev2 embedding face_info = self.app.get(image_bgr) if len(face_info) > 0: - face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * x['bbox'][3] - x['bbox'][1])[ + face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[ -1 ] # only use the maximum face id_ante_embedding = face_info['embedding'] diff --git a/pulid/pipeline_flux.py b/pulid/pipeline_flux.py new file mode 100644 index 0000000..a7cd1a5 --- /dev/null +++ b/pulid/pipeline_flux.py @@ -0,0 +1,186 @@ +import gc + +import cv2 +import insightface +import torch +import torch.nn as nn +from basicsr.utils import img2tensor, tensor2img +from facexlib.parsing import init_parsing_model +from facexlib.utils.face_restoration_helper import FaceRestoreHelper +from huggingface_hub import hf_hub_download, snapshot_download +from insightface.app import FaceAnalysis +from safetensors.torch import load_file +from torchvision.transforms import InterpolationMode +from torchvision.transforms.functional import normalize, resize + +from eva_clip import create_model_and_transforms +from eva_clip.constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD +from pulid.encoders_flux import IDFormer, PerceiverAttentionCA + + +class PuLIDPipeline(nn.Module): + def __init__(self, dit, device, weight_dtype=torch.bfloat16, *args, **kwargs): + super().__init__() + self.device = device + self.weight_dtype = weight_dtype + double_interval = 2 + single_interval = 4 + + # init encoder + self.pulid_encoder = IDFormer().to(self.device, self.weight_dtype) + + num_ca = 19 // double_interval + 38 // single_interval + if 19 % double_interval != 0: + num_ca += 1 + if 38 % single_interval != 0: + num_ca += 1 + self.pulid_ca = nn.ModuleList([ + PerceiverAttentionCA().to(self.device, self.weight_dtype) for _ in range(num_ca) + ]) + + dit.pulid_ca = self.pulid_ca + dit.pulid_double_interval = double_interval + dit.pulid_single_interval = single_interval + + # preprocessors + # face align and parsing + self.face_helper = FaceRestoreHelper( + upscale_factor=1, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + device=self.device, + ) + self.face_helper.face_parse = None + self.face_helper.face_parse = init_parsing_model(model_name='bisenet', device=self.device) + # clip-vit backbone + model, _, _ = create_model_and_transforms('EVA02-CLIP-L-14-336', 'eva_clip', force_custom_clip=True) + model = model.visual + self.clip_vision_model = model.to(self.device, dtype=self.weight_dtype) + eva_transform_mean = getattr(self.clip_vision_model, 'image_mean', OPENAI_DATASET_MEAN) + eva_transform_std = getattr(self.clip_vision_model, 'image_std', OPENAI_DATASET_STD) + if not isinstance(eva_transform_mean, (list, tuple)): + eva_transform_mean = (eva_transform_mean,) * 3 + if not isinstance(eva_transform_std, (list, tuple)): + eva_transform_std = (eva_transform_std,) * 3 + self.eva_transform_mean = eva_transform_mean + self.eva_transform_std = eva_transform_std + # antelopev2 + snapshot_download('DIAMONIK7777/antelopev2', local_dir='models/antelopev2') + self.app = FaceAnalysis( + name='antelopev2', root='.', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'] + ) + self.app.prepare(ctx_id=0, det_size=(640, 640)) + self.handler_ante = insightface.model_zoo.get_model('models/antelopev2/glintr100.onnx') + self.handler_ante.prepare(ctx_id=0) + + gc.collect() + torch.cuda.empty_cache() + + # self.load_pretrain() + + # other configs + self.debug_img_list = [] + + def load_pretrain(self, pretrain_path=None): + hf_hub_download('guozinan/PuLID', 'pulid_flux_v0.9.0.safetensors', local_dir='models') + ckpt_path = 'models/pulid_flux_v0.9.0.safetensors' + if pretrain_path is not None: + ckpt_path = pretrain_path + state_dict = load_file(ckpt_path) + state_dict_dict = {} + for k, v in state_dict.items(): + module = k.split('.')[0] + state_dict_dict.setdefault(module, {}) + new_k = k[len(module) + 1:] + state_dict_dict[module][new_k] = v + + for module in state_dict_dict: + print(f'loading from {module}') + getattr(self, module).load_state_dict(state_dict_dict[module], strict=True) + + del state_dict + del state_dict_dict + + def to_gray(self, img): + x = 0.299 * img[:, 0:1] + 0.587 * img[:, 1:2] + 0.114 * img[:, 2:3] + x = x.repeat(1, 3, 1, 1) + return x + + @torch.no_grad() + def get_id_embedding(self, image, cal_uncond=False): + """ + Args: + image: numpy rgb image, range [0, 255] + """ + self.face_helper.clean_all() + self.debug_img_list = [] + image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + # get antelopev2 embedding + face_info = self.app.get(image_bgr) + if len(face_info) > 0: + face_info = sorted(face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[ + -1 + ] # only use the maximum face + id_ante_embedding = face_info['embedding'] + self.debug_img_list.append( + image[ + int(face_info['bbox'][1]) : int(face_info['bbox'][3]), + int(face_info['bbox'][0]) : int(face_info['bbox'][2]), + ] + ) + else: + id_ante_embedding = None + + # using facexlib to detect and align face + self.face_helper.read_image(image_bgr) + self.face_helper.get_face_landmarks_5(only_center_face=True) + self.face_helper.align_warp_face() + if len(self.face_helper.cropped_faces) == 0: + raise RuntimeError('facexlib align face fail') + align_face = self.face_helper.cropped_faces[0] + # incase insightface didn't detect face + if id_ante_embedding is None: + print('fail to detect face using insightface, extract embedding on align face') + id_ante_embedding = self.handler_ante.get_feat(align_face) + + id_ante_embedding = torch.from_numpy(id_ante_embedding).to(self.device, self.weight_dtype) + if id_ante_embedding.ndim == 1: + id_ante_embedding = id_ante_embedding.unsqueeze(0) + + # parsing + input = img2tensor(align_face, bgr2rgb=True).unsqueeze(0) / 255.0 + input = input.to(self.device) + parsing_out = self.face_helper.face_parse(normalize(input, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]))[0] + parsing_out = parsing_out.argmax(dim=1, keepdim=True) + bg_label = [0, 16, 18, 7, 8, 9, 14, 15] + bg = sum(parsing_out == i for i in bg_label).bool() + white_image = torch.ones_like(input) + # only keep the face features + face_features_image = torch.where(bg, white_image, self.to_gray(input)) + self.debug_img_list.append(tensor2img(face_features_image, rgb2bgr=False)) + + # transform img before sending to eva-clip-vit + face_features_image = resize(face_features_image, self.clip_vision_model.image_size, InterpolationMode.BICUBIC) + face_features_image = normalize(face_features_image, self.eva_transform_mean, self.eva_transform_std) + id_cond_vit, id_vit_hidden = self.clip_vision_model( + face_features_image.to(self.weight_dtype), return_all_features=False, return_hidden=True, shuffle=False + ) + id_cond_vit_norm = torch.norm(id_cond_vit, 2, 1, True) + id_cond_vit = torch.div(id_cond_vit, id_cond_vit_norm) + + id_cond = torch.cat([id_ante_embedding, id_cond_vit], dim=-1) + + id_embedding = self.pulid_encoder(id_cond, id_vit_hidden) + + if not cal_uncond: + return id_embedding, None + + id_uncond = torch.zeros_like(id_cond) + id_vit_hidden_uncond = [] + for layer_idx in range(0, len(id_vit_hidden)): + id_vit_hidden_uncond.append(torch.zeros_like(id_vit_hidden[layer_idx])) + uncond_id_embedding = self.pulid_encoder(id_uncond, id_vit_hidden_uncond) + + return id_embedding, uncond_id_embedding diff --git a/requirements.txt b/requirements.txt index e64276a..8bdcb52 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ torch==2.0.1 torchvision==0.15.2 diffusers==0.25.0 -transformers==4.36.2 +transformers==4.43.3 gradio>=4.0.0 opencv-python httpx==0.23.3 @@ -14,3 +14,6 @@ insightface onnxruntime-gpu accelerate spaces==0.19.4 +SentencePiece +fire +safetensors \ No newline at end of file

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