[Flux] minor documentation fixes for flux. (#9048)

* minor documentation fixes for flux.

* clipskip

* add gist

docs/source/en/api/pipelines/flux.md

@@ -18,7 +18,7 @@ Original model checkpoints for Flux can be found [here](https://huggingface.co/b
 
 <Tip>
 
-Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.
+Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more.  For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c).
 
 </Tip>
 

src/diffusers/pipelines/flux/pipeline_flux.py

@@ -17,12 +17,7 @@ from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
 import torch
-from transformers import (
-    CLIPTextModel,
-    CLIPTokenizer,
-    T5EncoderModel,
-    T5TokenizerFast,
-)
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from ...image_processor import VaeImageProcessor
 from ...loaders import SD3LoraLoaderMixin
@@ -155,22 +150,18 @@ class FluxPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
         vae ([`AutoencoderKL`]):
             Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
-            with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
-            as its dimension.
-        text_encoder_2 ([`CLIPTextModelWithProjection`]):
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
-            specifically the
-            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
-            variant.
+        text_encoder ([`CLIPTextModel`]):
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_encoder_2 ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
         tokenizer (`CLIPTokenizer`):
             Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        tokenizer_2 (`CLIPTokenizer`):
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`T5TokenizerFast`):
             Second Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+            [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
     """
 
     model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
@@ -323,9 +314,6 @@ class FluxPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
             pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
                 Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                 If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
             lora_scale (`float`, *optional*):
                 A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
         """