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get decently far in changing variant stuff
This commit is contained in:
Edna
GitHub
@@ -40,7 +40,7 @@ from ...utils import (
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)
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from ...utils.torch_utils import randn_tensor
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from ..pipeline_utils import DiffusionPipeline
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from .pipeline_output import FluxPipelineOutput
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from .pipeline_output import ChromaPipelineOutput
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if is_torch_xla_available():
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@@ -57,15 +57,13 @@ EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import torch
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>>> from diffusers import FluxPipeline
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>>> from diffusers import ChromaPipeline
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>>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
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>>> pipe = ChromaPipeline.from_single_file("chroma-unlocked-v35-detail-calibrated.safetensors", torch_dtype=torch.bfloat16)
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>>> pipe.to("cuda")
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>>> prompt = "A cat holding a sign that says hello world"
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>>> # Depending on the variant being used, the pipeline call will slightly vary.
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>>> # Refer to the pipeline documentation for more details.
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>>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
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>>> image.save("flux.png")
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>>> image = pipe(prompt, num_inference_steps=28, guidance_scale=4.0).images[0]
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>>> image.save("chroma.png")
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```
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"""
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@@ -143,7 +141,7 @@ def retrieve_timesteps(
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return timesteps, num_inference_steps
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class FluxPipeline(
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class ChromaPipeline(
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DiffusionPipeline,
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FluxLoraLoaderMixin,
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FromSingleFileMixin,
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@@ -151,27 +149,21 @@ class FluxPipeline(
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FluxIPAdapterMixin,
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):
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r"""
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The Flux pipeline for text-to-image generation.
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The Chroma pipeline for text-to-image generation.
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Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
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Reference: https://huggingface.co/lodestones/Chroma/
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Args:
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transformer ([`FluxTransformer2DModel`]):
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transformer ([`ChromaTransformer2DModel`]):
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Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
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scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
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the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
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text_encoder_2 ([`T5EncoderModel`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representation
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text_encoder ([`T5EncoderModel`]):
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[T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
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the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
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tokenizer (`CLIPTokenizer`):
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Tokenizer of class
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[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
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tokenizer_2 (`T5TokenizerFast`):
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tokenizer (`T5TokenizerFast`):
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Second Tokenizer of class
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[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
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"""
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@@ -184,11 +176,9 @@ class FluxPipeline(
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self,
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scheduler: FlowMatchEulerDiscreteScheduler,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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text_encoder_2: T5EncoderModel,
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tokenizer_2: T5TokenizerFast,
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transformer: FluxTransformer2DModel,
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text_encoder: T5EncoderModel,
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tokenizer: T5TokenizerFast,
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transformer: ChromaTransformer2DModel,
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image_encoder: CLIPVisionModelWithProjection = None,
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feature_extractor: CLIPImageProcessor = None,
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variant: str = "flux",
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@@ -198,9 +188,7 @@ class FluxPipeline(
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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text_encoder_2=text_encoder_2,
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tokenizer=tokenizer,
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tokenizer_2=tokenizer_2,
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transformer=transformer,
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scheduler=scheduler,
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image_encoder=image_encoder,
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@@ -214,10 +202,6 @@ class FluxPipeline(
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self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
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)
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self.default_sample_size = 128
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if variant not in {"flux", "chroma"}:
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raise ValueError("`variant` must be `'flux' or `'chroma'`.")
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self.variant = variant
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def _get_chroma_attn_mask(self, length: torch.Tensor, max_sequence_length: int) -> torch.Tensor:
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attention_mask = torch.zeros((length.shape[0], max_sequence_length), dtype=torch.bool, device=length.device)
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@@ -248,7 +232,7 @@ class FluxPipeline(
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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return_length=(self.variant == "chroma"),
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return_length=True,
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return_overflowing_tokens=False,
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return_tensors="pt",
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)
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@@ -267,8 +251,6 @@ class FluxPipeline(
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output_hidden_states=False,
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attention_mask=(
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self._get_chroma_attn_mask(text_inputs.length, max_sequence_length).to(device)
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if self.variant == "chroma"
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else None
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),
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)[0]
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@@ -283,58 +265,12 @@ class FluxPipeline(
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return prompt_embeds
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def _get_clip_prompt_embeds(
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self,
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prompt: Union[str, List[str]],
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num_images_per_prompt: int = 1,
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device: Optional[torch.device] = None,
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):
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device = device or self._execution_device
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prompt = [prompt] if isinstance(prompt, str) else prompt
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batch_size = len(prompt)
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer_max_length,
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truncation=True,
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return_overflowing_tokens=False,
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return_length=False,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
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removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer_max_length} tokens: {removed_text}"
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)
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prompt_embeds = self.text_encoder(text_input_ids.to(device), output_hidden_states=False)
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# Use pooled output of CLIPTextModel
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prompt_embeds = prompt_embeds.pooler_output
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prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
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prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
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return prompt_embeds
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def encode_prompt(
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self,
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prompt: Union[str, List[str]],
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prompt_2: Union[str, List[str]],
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device: Optional[torch.device] = None,
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num_images_per_prompt: int = 1,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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max_sequence_length: int = 512,
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lora_scale: Optional[float] = None,
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):
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@@ -343,9 +279,6 @@ class FluxPipeline(
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
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used in all text-encoders
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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@@ -369,21 +302,11 @@ class FluxPipeline(
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# dynamically adjust the LoRA scale
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if self.text_encoder is not None and USE_PEFT_BACKEND:
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scale_lora_layers(self.text_encoder, lora_scale)
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if self.text_encoder_2 is not None and USE_PEFT_BACKEND:
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scale_lora_layers(self.text_encoder_2, lora_scale)
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prompt = [prompt] if isinstance(prompt, str) else prompt
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if prompt_embeds is None:
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prompt_2 = prompt_2 or prompt
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prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
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# We only use the pooled prompt output from the CLIPTextModel
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pooled_prompt_embeds = self._get_clip_prompt_embeds(
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prompt=prompt,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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)
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prompt_embeds = self._get_t5_prompt_embeds(
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prompt=prompt_2,
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num_images_per_prompt=num_images_per_prompt,
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@@ -396,15 +319,10 @@ class FluxPipeline(
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# Retrieve the original scale by scaling back the LoRA layers
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unscale_lora_layers(self.text_encoder, lora_scale)
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if self.text_encoder_2 is not None:
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if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
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# Retrieve the original scale by scaling back the LoRA layers
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unscale_lora_layers(self.text_encoder_2, lora_scale)
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dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
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text_ids = torch.zeros(prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
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return prompt_embeds, pooled_prompt_embeds, text_ids
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return prompt_embeds, text_ids
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def encode_image(self, image, device, num_images_per_prompt):
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dtype = next(self.image_encoder.parameters()).dtype
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@@ -456,15 +374,12 @@ class FluxPipeline(
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def check_inputs(
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self,
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prompt,
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prompt_2,
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height,
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width,
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negative_prompt=None,
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negative_prompt_2=None,
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prompt_embeds=None,
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negative_prompt_embeds=None,
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pooled_prompt_embeds=None,
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negative_pooled_prompt_embeds=None,
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callback_on_step_end_tensor_inputs=None,
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max_sequence_length=None,
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):
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@@ -485,39 +400,18 @@ class FluxPipeline(
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f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt_2 is not None and prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
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" only forward one of the two."
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)
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elif prompt is None and prompt_embeds is None:
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raise ValueError(
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"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
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)
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elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
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raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
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raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
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if negative_prompt is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
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raise ValueError(
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f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
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f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
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)
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if prompt_embeds is not None and pooled_prompt_embeds is None:
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raise ValueError(
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"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
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)
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if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
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raise ValueError(
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"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
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)
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if max_sequence_length is not None and max_sequence_length > 512:
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raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
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@@ -649,10 +543,7 @@ class FluxPipeline(
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def __call__(
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self,
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prompt: Union[str, List[str]] = None,
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prompt_2: Optional[Union[str, List[str]]] = None,
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negative_prompt: Union[str, List[str]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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true_cfg_scale: float = 1.0,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 28,
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@@ -662,13 +553,11 @@ class FluxPipeline(
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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ip_adapter_image: Optional[PipelineImageInput] = None,
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ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
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negative_ip_adapter_image: Optional[PipelineImageInput] = None,
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negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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joint_attention_kwargs: Optional[Dict[str, Any]] = None,
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@@ -683,18 +572,10 @@ class FluxPipeline(
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
|
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will be used instead.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `true_cfg_scale` is
|
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not greater than `1`).
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negative_prompt_2 (`str` or `List[str]`, *optional*):
|
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The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
|
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`text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
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true_cfg_scale (`float`, *optional*, defaults to 1.0):
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When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
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height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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The height in pixels of the generated image. This is set to 1024 by default for the best results.
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width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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@@ -724,9 +605,6 @@ class FluxPipeline(
|
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prompt_embeds (`torch.FloatTensor`, *optional*):
|
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
|
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provided, text embeddings will be generated from `prompt` input argument.
|
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pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
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Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
|
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If not provided, pooled text embeddings will be generated from `prompt` input argument.
|
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ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
|
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ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
|
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Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
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@@ -742,10 +620,6 @@ class FluxPipeline(
|
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
|
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argument.
|
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negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
|
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Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
|
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weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
|
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input argument.
|
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output_type (`str`, *optional*, defaults to `"pil"`):
|
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The output format of the generate image. Choose between
|
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
|
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@@ -769,7 +643,7 @@ class FluxPipeline(
|
||||
Examples:
|
||||
|
||||
Returns:
|
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[`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
|
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[`~pipelines.chroma.ChromaPipelineOutput`] or `tuple`: [`~pipelines.chroma.ChromaPipelineOutput`] if `return_dict`
|
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is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
|
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images.
|
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"""
|
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@@ -780,15 +654,11 @@ class FluxPipeline(
|
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# 1. Check inputs. Raise error if not correct
|
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self.check_inputs(
|
||||
prompt,
|
||||
prompt_2,
|
||||
height,
|
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width,
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negative_prompt=negative_prompt,
|
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negative_prompt_2=negative_prompt_2,
|
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prompt_embeds=prompt_embeds,
|
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negative_prompt_embeds=negative_prompt_embeds,
|
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pooled_prompt_embeds=pooled_prompt_embeds,
|
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
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callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
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max_sequence_length=max_sequence_length,
|
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)
|
||||
@@ -811,34 +681,25 @@ class FluxPipeline(
|
||||
lora_scale = (
|
||||
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
|
||||
)
|
||||
has_neg_prompt = negative_prompt is not None or (
|
||||
negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
|
||||
)
|
||||
do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
|
||||
do_cfg = guidance_scale > 1
|
||||
(
|
||||
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,
|
||||
)
|
||||
if do_true_cfg:
|
||||
if do_cfg:
|
||||
(
|
||||
negative_prompt_embeds,
|
||||
negative_pooled_prompt_embeds,
|
||||
negative_text_ids,
|
||||
) = self.encode_prompt(
|
||||
prompt=negative_prompt,
|
||||
prompt_2=negative_prompt_2,
|
||||
prompt_embeds=negative_prompt_embeds,
|
||||
pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
||||
device=device,
|
||||
num_images_per_prompt=num_images_per_prompt,
|
||||
max_sequence_length=max_sequence_length,
|
||||
@@ -933,7 +794,6 @@ class FluxPipeline(
|
||||
hidden_states=latents,
|
||||
timestep=timestep / 1000,
|
||||
guidance=guidance,
|
||||
pooled_projections=pooled_prompt_embeds,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
txt_ids=text_ids,
|
||||
img_ids=latent_image_ids,
|
||||
@@ -941,7 +801,7 @@ class FluxPipeline(
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
if do_true_cfg:
|
||||
if do_cfg:
|
||||
if negative_image_embeds is not None:
|
||||
self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
|
||||
neg_noise_pred = self.transformer(
|
||||
|
||||
Reference in New Issue
Block a user