Merge branch 'main' into allow-device-placement-bnb

Author: sayakpaul

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

@@ -30,15 +30,17 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.m
 This pipeline was contributed by [zRzRzRzRzRzRzR](https://github.com/zRzRzRzRzRzRzR). The original codebase can be found [here](https://huggingface.co/THUDM). The original weights can be found under [hf.co/THUDM](https://huggingface.co/THUDM).
 
 There are three official CogVideoX checkpoints for text-to-video and video-to-video.
+
 | checkpoints | recommended inference dtype |
-|---|---|
+|:---:|:---:|
 | [`THUDM/CogVideoX-2b`](https://huggingface.co/THUDM/CogVideoX-2b) | torch.float16 |
 | [`THUDM/CogVideoX-5b`](https://huggingface.co/THUDM/CogVideoX-5b) | torch.bfloat16 |
 | [`THUDM/CogVideoX1.5-5b`](https://huggingface.co/THUDM/CogVideoX1.5-5b) | torch.bfloat16 |
 
 There are two official CogVideoX checkpoints available for image-to-video.
+
 | checkpoints | recommended inference dtype |
-|---|---|
+|:---:|:---:|
 | [`THUDM/CogVideoX-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-5b-I2V) | torch.bfloat16 |
 | [`THUDM/CogVideoX-1.5-5b-I2V`](https://huggingface.co/THUDM/CogVideoX-1.5-5b-I2V) | torch.bfloat16 |
 
@@ -48,8 +50,9 @@ For the CogVideoX 1.5 series:
 - Both T2V and I2V models support generation with 81 and 161 frames and work best at this value. Exporting videos at 16 FPS is recommended.
 
 There are two official CogVideoX checkpoints that support pose controllable generation (by the [Alibaba-PAI](https://huggingface.co/alibaba-pai) team).
+
 | checkpoints | recommended inference dtype |
-|---|---|
+|:---:|:---:|
 | [`alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose) | torch.bfloat16 |
 | [`alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose`](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose) | torch.bfloat16 |
 

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

@@ -148,7 +148,7 @@ image.save("output.png")
 **Note:** `black-forest-labs/Flux.1-Depth-dev` is _not_ a ControlNet model. [`ControlNetModel`] models are a separate component from the UNet/Transformer whose residuals are added to the actual underlying model. Depth Control is an alternate architecture that achieves effectively the same results as a ControlNet model would, by using channel-wise concatenation with input control condition and ensuring the transformer learns structure control by following the condition as closely as possible.
 
 ```python
-# !pip install git+https://github.com/asomoza/image_gen_aux.git
+# !pip install git+https://github.com/huggingface/image_gen_aux
 import torch
 from diffusers import FluxControlPipeline, FluxTransformer2DModel
 from diffusers.utils import load_image

examples/community/README.md

@@ -3,20 +3,20 @@
 
 import torch
 import torchvision.transforms.functional as FF
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
 
 from diffusers import StableDiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import USE_PEFT_BACKEND
 
 
 try:
     from compel import Compel
 except ImportError:
     Compel = None
 
+KBASE = "ADDBASE"
 KCOMM = "ADDCOMM"
 KBRK = "BREAK"
 
@@ -34,6 +34,11 @@ class RegionalPromptingStableDiffusionPipeline(StableDiffusionPipeline):
 
         Optional
             rp_args["save_mask"]: True/False (save masks in prompt mode)
+            rp_args["power"]: int (power for attention maps in prompt mode)
+            rp_args["base_ratio"]:
+                float (Sets the ratio of the base prompt)
+                ex) 0.2 (20%*BASE_PROMPT + 80%*REGION_PROMPT)
+                [Use base prompt](https://github.com/hako-mikan/sd-webui-regional-prompter?tab=readme-ov-file#use-base-prompt)
 
     Pipeline for text-to-image generation using Stable Diffusion.
 
@@ -70,6 +75,7 @@ def __init__(
         scheduler: KarrasDiffusionSchedulers,
         safety_checker: StableDiffusionSafetyChecker,
         feature_extractor: CLIPImageProcessor,
+        image_encoder: CLIPVisionModelWithProjection = None,
         requires_safety_checker: bool = True,
     ):
         super().__init__(
@@ -80,6 +86,7 @@ def __init__(
             scheduler,
             safety_checker,
             feature_extractor,
+            image_encoder,
             requires_safety_checker,
         )
         self.register_modules(
@@ -90,6 +97,7 @@ def __init__(
             scheduler=scheduler,
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
         )
 
     @torch.no_grad()
@@ -110,17 +118,40 @@ def __call__(
         rp_args: Dict[str, str] = None,
     ):
         active = KBRK in prompt[0] if isinstance(prompt, list) else KBRK in prompt
+        use_base = KBASE in prompt[0] if isinstance(prompt, list) else KBASE in prompt
         if negative_prompt is None:
             negative_prompt = "" if isinstance(prompt, str) else [""] * len(prompt)
 
         device = self._execution_device
         regions = 0
 
+        self.base_ratio = float(rp_args["base_ratio"]) if "base_ratio" in rp_args else 0.0
         self.power = int(rp_args["power"]) if "power" in rp_args else 1
 
         prompts = prompt if isinstance(prompt, list) else [prompt]
-        n_prompts = negative_prompt if isinstance(prompt, str) else [negative_prompt]
+        n_prompts = negative_prompt if isinstance(prompt, list) else [negative_prompt]
         self.batch = batch = num_images_per_prompt * len(prompts)
+
+        if use_base:
+            bases = prompts.copy()
+            n_bases = n_prompts.copy()
+
+            for i, prompt in enumerate(prompts):
+                parts = prompt.split(KBASE)
+                if len(parts) == 2:
+                    bases[i], prompts[i] = parts
+                elif len(parts) > 2:
+                    raise ValueError(f"Multiple instances of {KBASE} found in prompt: {prompt}")
+            for i, prompt in enumerate(n_prompts):
+                n_parts = prompt.split(KBASE)
+                if len(n_parts) == 2:
+                    n_bases[i], n_prompts[i] = n_parts
+                elif len(n_parts) > 2:
+                    raise ValueError(f"Multiple instances of {KBASE} found in negative prompt: {prompt}")
+
+            all_bases_cn, _ = promptsmaker(bases, num_images_per_prompt)
+            all_n_bases_cn, _ = promptsmaker(n_bases, num_images_per_prompt)
+
         all_prompts_cn, all_prompts_p = promptsmaker(prompts, num_images_per_prompt)
         all_n_prompts_cn, _ = promptsmaker(n_prompts, num_images_per_prompt)
 
@@ -137,8 +168,16 @@ def getcompelembs(prps):
 
             conds = getcompelembs(all_prompts_cn)
             unconds = getcompelembs(all_n_prompts_cn)
-            embs = getcompelembs(prompts)
-            n_embs = getcompelembs(n_prompts)
+            base_embs = getcompelembs(all_bases_cn) if use_base else None
+            base_n_embs = getcompelembs(all_n_bases_cn) if use_base else None
+            # When using base, it seems more reasonable to use base prompts as prompt_embeddings rather than regional prompts
+            embs = getcompelembs(prompts) if not use_base else base_embs
+            n_embs = getcompelembs(n_prompts) if not use_base else base_n_embs
+
+            if use_base and self.base_ratio > 0:
+                conds = self.base_ratio * base_embs + (1 - self.base_ratio) * conds
+                unconds = self.base_ratio * base_n_embs + (1 - self.base_ratio) * unconds
+
             prompt = negative_prompt = None
         else:
             conds = self.encode_prompt(prompts, device, 1, True)[0]
@@ -147,6 +186,18 @@ def getcompelembs(prps):
                 if equal
                 else self.encode_prompt(all_n_prompts_cn, device, 1, True)[0]
             )
+
+            if use_base and self.base_ratio > 0:
+                base_embs = self.encode_prompt(bases, device, 1, True)[0]
+                base_n_embs = (
+                    self.encode_prompt(n_bases, device, 1, True)[0]
+                    if equal
+                    else self.encode_prompt(all_n_bases_cn, device, 1, True)[0]
+                )
+
+                conds = self.base_ratio * base_embs + (1 - self.base_ratio) * conds
+                unconds = self.base_ratio * base_n_embs + (1 - self.base_ratio) * unconds
+
             embs = n_embs = None
 
         if not active:
@@ -225,8 +276,6 @@ def forward(
 
                     residual = hidden_states
 
-                    args = () if USE_PEFT_BACKEND else (scale,)
-
                     if attn.spatial_norm is not None:
                         hidden_states = attn.spatial_norm(hidden_states, temb)
 
@@ -247,16 +296,15 @@ def forward(
                     if attn.group_norm is not None:
                         hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
 
-                    args = () if USE_PEFT_BACKEND else (scale,)
-                    query = attn.to_q(hidden_states, *args)
+                    query = attn.to_q(hidden_states)
 
                     if encoder_hidden_states is None:
                         encoder_hidden_states = hidden_states
                     elif attn.norm_cross:
                         encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
 
-                    key = attn.to_k(encoder_hidden_states, *args)
-                    value = attn.to_v(encoder_hidden_states, *args)
+                    key = attn.to_k(encoder_hidden_states)
+                    value = attn.to_v(encoder_hidden_states)
 
                     inner_dim = key.shape[-1]
                     head_dim = inner_dim // attn.heads
@@ -283,7 +331,7 @@ def forward(
                     hidden_states = hidden_states.to(query.dtype)
 
                     # linear proj
-                    hidden_states = attn.to_out[0](hidden_states, *args)
+                    hidden_states = attn.to_out[0](hidden_states)
                     # dropout
                     hidden_states = attn.to_out[1](hidden_states)
 
@@ -410,9 +458,9 @@ def promptsmaker(prompts, batch):
         add = ""
         if KCOMM in prompt:
             add, prompt = prompt.split(KCOMM)
-            add = add + " "
-        prompts = prompt.split(KBRK)
-        out_p.append([add + p for p in prompts])
+            add = add.strip() + " "
+        prompts = [p.strip() for p in prompt.split(KBRK)]
+        out_p.append([add + p for i, p in enumerate(prompts)])
     out = [None] * batch * len(out_p[0]) * len(out_p)
     for p, prs in enumerate(out_p):  # inputs prompts
         for r, pr in enumerate(prs):  # prompts for regions
@@ -449,7 +497,6 @@ def startend(cells, array):
             add = []
             startend(add, inratios[1:])
             icells.append(add)
-
     return ocells, icells, sum(len(cell) for cell in icells)