diffusers, forced sd xl

diffusers_holder.py

@@ -0,0 +1,416 @@
+# Copyright 2022 Lunar Ring. All rights reserved.
+# Written by Johannes Stelzer, email stelzer@lunar-ring.ai twitter @j_stelzer
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import torch
+torch.backends.cudnn.benchmark = False
+torch.set_grad_enabled(False)
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+import warnings
+import torch
+from PIL import Image
+import torch
+from typing import Optional
+from torch import autocast
+from contextlib import nullcontext
+from utils import interpolate_spherical
+from diffusers import DiffusionPipeline
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+
+
+class DiffusersHolder():
+    def __init__(self, pipe):
+        # Base settings
+        self.negative_prompt = ""
+        self.guidance_scale = 5.0
+        self.num_inference_steps = 30
+        
+        # Check if valid pipe
+        self.pipe = pipe
+        self.device = str(pipe._execution_device)
+        self.init_type_pipe()
+        self.init_dtype()
+        
+        self.width_latent = self.pipe.unet.config.sample_size
+        self.height_latent = self.pipe.unet.config.sample_size
+        
+        
+
+    def init_type_pipe(self):
+        self.type_pipe = "StableDiffusionXLPipeline"
+        if self.type_pipe == "StableDiffusionXLPipeline":
+            self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
+            self.use_sd_xl = True
+        else:
+            self.use_sd_xl = False
+
+    def init_dtype(self):
+        if self.type_pipe == "StableDiffusionXLPipeline":
+            prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
+            self.dtype = prompt_embeds.dtype
+
+    def set_num_inference_steps(self, num_inference_steps):
+        self.num_inference_steps = num_inference_steps
+        if self.use_sd_xl:
+            self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
+
+
+    def set_dimensions(self, width, height):
+        s = self.pipe.vae_scale_factor
+        if width is None:
+            self.width_latent = self.pipe.unet.config.sample_size 
+            self.width_img = self.width_latent * self.pipe.vae_scale_factor
+        else:
+            self.width_img = int(round(width / s) * s)
+            self.width_latent = int(self.width_img / s)
+            
+        if height is None:
+            self.height_latent = self.pipe.unet.config.sample_size 
+            self.height_img = self.width_latent * self.pipe.vae_scale_factor
+        else:
+            self.height_img = int(round(height / s) * s)
+            self.height_latent = int(self.height_img / s)
+        
+
+    def set_negative_prompt(self, negative_prompt):
+        r"""Set the negative prompt. Currenty only one negative prompt is supported
+        """
+        if isinstance(negative_prompt, str):
+            self.negative_prompt = [negative_prompt]
+        else:
+            self.negative_prompt = negative_prompt
+
+        if len(self.negative_prompt) > 1:
+            self.negative_prompt = [self.negative_prompt[0]]
+
+    def get_text_embedding(self, prompt, do_classifier_free_guidance=True):
+        if self.use_sd_xl:
+            pr_encoder = self.pipe.encode_prompt
+        else:
+            pr_encoder = self.pipe._encode_prompt
+
+        prompt_embeds = pr_encoder(
+            prompt,
+            self.device,
+            1,
+            do_classifier_free_guidance,
+            negative_prompt=self.negative_prompt,
+            prompt_embeds=None,
+            negative_prompt_embeds=None,
+            lora_scale=None,
+        )
+        return prompt_embeds
+
+
+    def get_noise(self, seed=420, mode=None):
+        H = self.height_latent
+        W = self.width_latent
+        C = self.pipe.unet.config.in_channels
+        generator = torch.Generator(device=self.device).manual_seed(int(seed))
+        latents = torch.randn((1, C, H, W), generator=generator, dtype=self.dtype, device=self.device)
+        if self.use_sd_xl:
+            latents = latents * self.pipe.scheduler.init_noise_sigma
+        return latents
+
+    @torch.no_grad()
+    def latent2image(
+            self,
+            latents: torch.FloatTensor):
+        r"""
+        Returns an image provided a latent representation from diffusion.
+        Args:
+            latents: torch.FloatTensor
+                Result of the diffusion process.
+        """
+        if self.use_sd_xl:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            self.pipe.vae.to(dtype=torch.float32)
+
+            use_torch_2_0_or_xformers = isinstance(
+                self.pipe.vae.decoder.mid_block.attentions[0].processor,
+                (
+                    AttnProcessor2_0,
+                    XFormersAttnProcessor,
+                    LoRAXFormersAttnProcessor,
+                    LoRAAttnProcessor2_0,
+                ),
+            )
+            # if xformers or torch_2_0 is used attention block does not need
+            # to be in float32 which can save lots of memory
+            if use_torch_2_0_or_xformers:
+                self.pipe.vae.post_quant_conv.to(latents.dtype)
+                self.pipe.vae.decoder.conv_in.to(latents.dtype)
+                self.pipe.vae.decoder.mid_block.to(latents.dtype)
+            else:
+                latents = latents.float()
+
+        image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
+        image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])
+        
+        
+        
+        return np.asarray(image[0])
+
+    @torch.no_grad()
+    def run_diffusion_standard(
+            self,
+            text_embeddings: torch.FloatTensor,
+            latents_start: torch.FloatTensor,
+            idx_start: int = 0,
+            list_latents_mixing=None,
+            mixing_coeffs=0.0,
+            return_image: Optional[bool] = False):
+        
+        if type(mixing_coeffs) == float:
+            list_mixing_coeffs = (1+self.num_inference_steps) * [mixing_coeffs]
+        elif type(mixing_coeffs) == list:
+            assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
+            list_mixing_coeffs = mixing_coeffs
+        else:
+            raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
+
+        if np.sum(list_mixing_coeffs) > 0:
+            assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}"
+            
+        do_classifier_free_guidance = self.guidance_scale > 1.0
+        
+        # diffusers bit wiggly
+        self.pipe.scheduler.set_timesteps(self.num_inference_steps-1, device=self.device)
+        timesteps = self.pipe.scheduler.timesteps
+        
+        if len(timesteps) != self.num_inference_steps:
+            self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
+            timesteps = self.pipe.scheduler.timesteps
+        
+        latents = latents_start.clone()
+        list_latents_out = []
+
+        num_warmup_steps = len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order
+        for i, t in enumerate(timesteps):
+            # Set the right starting latents
+            if i < idx_start:
+                list_latents_out.append(None)
+                continue
+            elif i == idx_start:
+                latents = latents_start.clone()
+            # Mix latents
+            if i > 0 and list_mixing_coeffs[i] > 0:
+                latents_mixtarget = list_latents_mixing[i - 1].clone()
+                latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
+                
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
+        
+            # predict the noise residual
+            noise_pred = self.pipe.unet(
+                latent_model_input,
+                t,
+                encoder_hidden_states=text_embeddings,
+                return_dict=False,
+            )[0]
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.pipe.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+            list_latents_out.append(latents.clone())
+        
+        if return_image:
+            return self.latent2image(latents)
+        else:
+            return list_latents_out
+
+    @torch.no_grad()
+    def run_diffusion_sd_xl(
+            self,
+            text_embeddings: list,
+            latents_start: torch.FloatTensor,
+            idx_start: int = 0,
+            list_latents_mixing=None,
+            mixing_coeffs=0.0,
+            return_image: Optional[bool] = False):
+        
+        # prompt = "photo of a house"
+        # self.num_inference_steps = 50
+        # mixing_coeffs= 0.0
+        # idx_start= 0
+        # latents_start = self.get_noise()
+        # text_embeddings = self.pipe.encode_prompt(
+        #     prompt,
+        #     self.device,
+        #     num_images_per_prompt=1,
+        #     do_classifier_free_guidance=True,
+        #     negative_prompt="",
+        #     prompt_embeds=None,
+        #     negative_prompt_embeds=None,
+        #     pooled_prompt_embeds=None,
+        #     negative_pooled_prompt_embeds=None,
+        #     lora_scale=None,
+        # )
+        
+
+        # 0. Default height and width to unet
+        original_size = (1024, 1024)  # FIXME
+        crops_coords_top_left = (0, 0) # FIXME
+        target_size = original_size
+        batch_size = 1
+        eta = 0.0
+        num_images_per_prompt = 1
+        cross_attention_kwargs = None
+        generator = torch.Generator(device=self.device)  # dummy generator
+        do_classifier_free_guidance = self.guidance_scale > 1.0
+        
+        # 1. Check inputs. Raise error if not correct & 2. Define call parameters
+        # FIXME see if check_inputs use
+        if type(mixing_coeffs) == float:
+            list_mixing_coeffs = (1+self.num_inference_steps) * [mixing_coeffs]
+        elif type(mixing_coeffs) == list:
+            assert len(mixing_coeffs) == self.num_inference_steps, f"len(mixing_coeffs) {len(mixing_coeffs)} != self.num_inference_steps {self.num_inference_steps}"
+            list_mixing_coeffs = mixing_coeffs
+        else:
+            raise ValueError("mixing_coeffs should be float or list with len=num_inference_steps")
+
+        if np.sum(list_mixing_coeffs) > 0:
+            assert len(list_latents_mixing) == self.num_inference_steps, f"len(list_latents_mixing) {len(list_latents_mixing)} != self.num_inference_steps {self.num_inference_steps}"
+
+        # 3. Encode input prompt (already encoded outside bc of mixing, just split here)
+        prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
+
+        # 4. Prepare timesteps
+        self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
+        timesteps = self.pipe.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        latents = latents_start.clone()
+        list_latents_out = []
+
+        # 6. Prepare extra step kwargs. usedummy generator
+        extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)  # dummy
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        add_time_ids = self.pipe._get_add_time_ids(
+            original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
+        )
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(self.device)
+        add_text_embeds = add_text_embeds.to(self.device)
+        add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
+
+        # 8. Denoising loop
+        for i, t in enumerate(timesteps):
+            # Set the right starting latents
+            if i < idx_start:
+                list_latents_out.append(None)
+                continue
+            elif i == idx_start:
+                latents = latents_start.clone()
+
+
+
+            # Mix latents for crossfeeding
+            if i > 0 and list_mixing_coeffs[i] > 0:
+                latents_mixtarget = list_latents_mixing[i - 1].clone()
+                latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
+
+
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+            # Always scale latents
+            latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+            noise_pred = self.pipe.unet(
+                latent_model_input,
+                t,
+                encoder_hidden_states=prompt_embeds,
+                cross_attention_kwargs=cross_attention_kwargs,
+                added_cond_kwargs=added_cond_kwargs,
+                return_dict=False,
+            )[0]
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # FIXME guidance_rescale disabled
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+            # Append latents
+            list_latents_out.append(latents.clone())
+        
+        if return_image:
+            return self.latent2image(latents)
+        else:
+            return list_latents_out
+
+
+
+
+#%%
+
+if __name__ == "__main__":
+    pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
+    pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
+    pipe.to('cuda')
+    # xxx
+    self = DiffusersHolder(pipe)
+    # xxx
+    self.set_num_inference_steps(50)
+    self.set_dimensions(1536, 1024)
+    prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
+    text_embeddings = self.get_text_embedding(prompt)
+    generator = torch.Generator(device=self.device).manual_seed(int(420))
+    latents_start = self.get_noise()
+    list_latents_1 = self.run_diffusion_sd_xl(text_embeddings, latents_start)
+    img_orig = self.latent2image(list_latents_1[-1])
+    
+    
+    
+    # %%
+    
+    """
+    OPEN
+        - other examples
+        - kill upscaling? or keep?
+        - cleanup
+            - ldh
+            - sdh class
+            - diffusion holder
+            - check linting
+            - check docstrings
+        - fix readme
+    """    
+    
+    
+   

example1_standard.py

@@ -20,33 +20,37 @@ import warnings
 warnings.filterwarnings('ignore')
 import warnings
 from latent_blending import LatentBlending
-from stable_diffusion_holder import StableDiffusionHolder
-from huggingface_hub import hf_hub_download
-
+from diffusers_holder import DiffusersHolder
+from diffusers import DiffusionPipeline
 
 # %% First let us spawn a stable diffusion holder. Uncomment your version of choice.
-# fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1-base", filename="v2-1_512-ema-pruned.ckpt")
-fp_ckpt = hf_hub_download(repo_id="stabilityai/stable-diffusion-2-1", filename="v2-1_768-ema-pruned.ckpt")
-sdh = StableDiffusionHolder(fp_ckpt)
+# dh = DiffusersHolder("stabilityai/stable-diffusion-xl-base-0.9")
+pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
+pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
+pipe.to('cuda')
+dh = DiffusersHolder(pipe)
 # %% Next let's set up all parameters
-depth_strength = 0.65  # Specifies how deep (in terms of diffusion iterations the first branching happens)
-t_compute_max_allowed = 15  # Determines the quality of the transition in terms of compute time you grant it
-fixed_seeds = [69731932, 504430820]
+depth_strength = 0.55  # Specifies how deep (in terms of diffusion iterations the first branching happens)
+t_compute_max_allowed = 60  # Determines the quality of the transition in terms of compute time you grant it
+fixed_seeds = [6913192, 504443080]
+num_inference_steps = 50
 
-prompt1 = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
-prompt2 = "photo of an golden statue with a funny hat, surrounded by ferns and vines, grainy analog photograph, mystical ambience, incredible detail"
+prompt1 = "underwater landscape, fish, und the sea, incredible detail, high resolution"
+prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
 
 fp_movie = 'movie_example1.mp4'
 duration_transition = 12  # In seconds
 
 # Spawn latent blending
-lb = LatentBlending(sdh)
+lb = LatentBlending(dh)
 lb.set_prompt1(prompt1)
 lb.set_prompt2(prompt2)
+lb.set_dimensions(1536, 1024)
 
 # Run latent blending
 lb.run_transition(
     depth_strength=depth_strength,
+    num_inference_steps=num_inference_steps,
     t_compute_max_allowed=t_compute_max_allowed,
     fixed_seeds=fixed_seeds)
 

latent_blending.py

@@ -26,7 +26,6 @@ from tqdm.auto import tqdm
 from PIL import Image
 from movie_util import MovieSaver
 from typing import List, Optional
-from ldm.models.diffusion.ddpm import LatentUpscaleDiffusion, LatentInpaintDiffusion
 import lpips
 from utils import interpolate_spherical, interpolate_linear, add_frames_linear_interp, yml_load, yml_save
 
@@ -34,7 +33,7 @@ from utils import interpolate_spherical, interpolate_linear, add_frames_linear_i
 class LatentBlending():
     def __init__(
             self,
-            sdh: None,
+            dh: None,
             guidance_scale: float = 4,
             guidance_scale_mid_damper: float = 0.5,
             mid_compression_scaler: float = 1.2):
@@ -59,10 +58,10 @@ class LatentBlending():
             and guidance_scale_mid_damper <= 1.0, \
             f"guidance_scale_mid_damper neees to be in interval (0,1], you provided {guidance_scale_mid_damper}"
 
-        self.sdh = sdh
-        self.device = self.sdh.device
-        self.width = self.sdh.width
-        self.height = self.sdh.height
+        self.dh = dh
+        self.device = self.dh.device
+        self.set_dimensions()
+
         self.guidance_scale_mid_damper = guidance_scale_mid_damper
         self.mid_compression_scaler = mid_compression_scaler
         self.seed1 = 0
@@ -86,40 +85,49 @@ class LatentBlending():
         self.image1_lowres = None
         self.image2_lowres = None
         self.negative_prompt = None
-        self.num_inference_steps = self.sdh.num_inference_steps
+        self.num_inference_steps = self.dh.num_inference_steps
         self.noise_level_upscaling = 20
         self.list_injection_idx = None
         self.list_nmb_branches = None
 
         # Mixing parameters
-        self.branch1_crossfeed_power = 0.1
-        self.branch1_crossfeed_range = 0.6
-        self.branch1_crossfeed_decay = 0.8
+        self.branch1_crossfeed_power = 0.05
+        self.branch1_crossfeed_range = 0.4
+        self.branch1_crossfeed_decay = 0.9
 
         self.parental_crossfeed_power = 0.1
         self.parental_crossfeed_range = 0.8
         self.parental_crossfeed_power_decay = 0.8
 
         self.set_guidance_scale(guidance_scale)
-        self.init_mode()
+        self.mode = 'standard'
+        # self.init_mode()
         self.multi_transition_img_first = None
         self.multi_transition_img_last = None
         self.dt_per_diff = 0
         self.spatial_mask = None
         self.lpips = lpips.LPIPS(net='alex').cuda(self.device)
+        
+        self.set_prompt1("")
+        self.set_prompt2("")
 
-    def init_mode(self):
-        r"""
-        Sets the operational mode. Currently supported are standard, inpainting and x4 upscaling.
-        """
-        if isinstance(self.sdh.model, LatentUpscaleDiffusion):
-            self.mode = 'upscale'
-        elif isinstance(self.sdh.model, LatentInpaintDiffusion):
-            self.sdh.image_source = None
-            self.sdh.mask_image = None
-            self.mode = 'inpaint'
-        else:
-            self.mode = 'standard'
+    # def init_mode(self):
+    #     r"""
+    #     Sets the operational mode. Currently supported are standard, inpainting and x4 upscaling.
+    #     """
+    #     if isinstance(self.dh.model, LatentUpscaleDiffusion):
+    #         self.mode = 'upscale'
+    #     elif isinstance(self.dh.model, LatentInpaintDiffusion):
+    #         self.dh.image_source = None
+    #         self.dh.mask_image = None
+    #         self.mode = 'inpaint'
+    #     else:
+    #         self.mode = 'standard'
+    
+    def set_dimensions(self, width=None, height=None):
+        self.dh.set_dimensions(width, height)
+
+        
 
     def set_guidance_scale(self, guidance_scale):
         r"""
@@ -127,13 +135,13 @@ class LatentBlending():
         """
         self.guidance_scale_base = guidance_scale
         self.guidance_scale = guidance_scale
-        self.sdh.guidance_scale = guidance_scale
+        self.dh.guidance_scale = guidance_scale
 
     def set_negative_prompt(self, negative_prompt):
         r"""Set the negative prompt. Currenty only one negative prompt is supported
         """
         self.negative_prompt = negative_prompt
-        self.sdh.set_negative_prompt(negative_prompt)
+        self.dh.set_negative_prompt(negative_prompt)
 
     def set_guidance_mid_dampening(self, fract_mixing):
         r"""
@@ -144,7 +152,7 @@ class LatentBlending():
         max_guidance_reduction = self.guidance_scale_base * (1 - self.guidance_scale_mid_damper) - 1
         guidance_scale_effective = self.guidance_scale_base - max_guidance_reduction * mid_factor
         self.guidance_scale = guidance_scale_effective
-        self.sdh.guidance_scale = guidance_scale_effective
+        self.dh.guidance_scale = guidance_scale_effective
 
     def set_branch1_crossfeed(self, crossfeed_power, crossfeed_range, crossfeed_decay):
         r"""
@@ -265,7 +273,7 @@ class LatentBlending():
 
         # Ensure correct num_inference_steps in holder
         self.num_inference_steps = num_inference_steps
-        self.sdh.num_inference_steps = num_inference_steps
+        self.dh.set_num_inference_steps(num_inference_steps)
 
         # Compute / Recycle first image
         if not recycle_img1 or len(self.tree_latents[0]) != self.num_inference_steps:
@@ -282,7 +290,7 @@ class LatentBlending():
         # Reset the tree, injecting the edge latents1/2 we just generated/recycled
         self.tree_latents = [list_latents1, list_latents2]
         self.tree_fracts = [0.0, 1.0]
-        self.tree_final_imgs = [self.sdh.latent2image((self.tree_latents[0][-1])), self.sdh.latent2image((self.tree_latents[-1][-1]))]
+        self.tree_final_imgs = [self.dh.latent2image((self.tree_latents[0][-1])), self.dh.latent2image((self.tree_latents[-1][-1]))]
         self.tree_idx_injection = [0, 0]
 
         # Hard-fix. Apply spatial mask only for list_latents2 but not for transition. WIP...
@@ -325,7 +333,7 @@ class LatentBlending():
         self.dt_per_diff = (t1 - t0) / self.num_inference_steps
         self.tree_latents[0] = list_latents1
         if return_image:
-            return self.sdh.latent2image(list_latents1[-1])
+            return self.dh.latent2image(list_latents1[-1])
         else:
             return list_latents1
 
@@ -357,7 +365,7 @@ class LatentBlending():
         self.tree_latents[-1] = list_latents2
 
         if return_image:
-            return self.sdh.latent2image(list_latents2[-1])
+            return self.dh.latent2image(list_latents2[-1])
         else:
             return list_latents2
 
@@ -511,55 +519,17 @@ class LatentBlending():
         """
         b_parent1, b_parent2 = self.get_closest_idx(fract_mixing)
         self.tree_latents.insert(b_parent1 + 1, list_latents)
-        self.tree_final_imgs.insert(b_parent1 + 1, self.sdh.latent2image(list_latents[-1]))
+        self.tree_final_imgs.insert(b_parent1 + 1, self.dh.latent2image(list_latents[-1]))
         self.tree_fracts.insert(b_parent1 + 1, fract_mixing)
         self.tree_idx_injection.insert(b_parent1 + 1, idx_injection)
 
-    def get_spatial_mask_template(self):
-        r"""
-        Experimental helper function to get a spatial mask template.
-        """
-        shape_latents = [self.sdh.C, self.sdh.height // self.sdh.f, self.sdh.width // self.sdh.f]
-        C, H, W = shape_latents
-        return np.ones((H, W))
-
-    def set_spatial_mask(self, img_mask):
-        r"""
-        Experimental helper function to set a spatial mask.
-        The mask forces latents to be overwritten.
-        Args:
-            img_mask:
-                mask image [0,1]. You can get a template using get_spatial_mask_template
-        """
-        shape_latents = [self.sdh.C, self.sdh.height // self.sdh.f, self.sdh.width // self.sdh.f]
-        C, H, W = shape_latents
-        img_mask = np.asarray(img_mask)
-        assert len(img_mask.shape) == 2, "Currently, only 2D images are supported as mask"
-        img_mask = np.clip(img_mask, 0, 1)
-        assert img_mask.shape[0] == H, f"Your mask needs to be of dimension {H} x {W}"
-        assert img_mask.shape[1] == W, f"Your mask needs to be of dimension {H} x {W}"
-        spatial_mask = torch.from_numpy(img_mask).to(device=self.device)
-        spatial_mask = torch.unsqueeze(spatial_mask, 0)
-        spatial_mask = spatial_mask.repeat((C, 1, 1))
-        spatial_mask = torch.unsqueeze(spatial_mask, 0)
-        self.spatial_mask = spatial_mask
-
     def get_noise(self, seed):
         r"""
         Helper function to get noise given seed.
         Args:
             seed: int
         """
-        generator = torch.Generator(device=self.sdh.device).manual_seed(int(seed))
-        if self.mode == 'standard':
-            shape_latents = [self.sdh.C, self.sdh.height // self.sdh.f, self.sdh.width // self.sdh.f]
-            C, H, W = shape_latents
-        elif self.mode == 'upscale':
-            w = self.image1_lowres.size[0]
-            h = self.image1_lowres.size[1]
-            shape_latents = [self.sdh.model.channels, h, w]
-            C, H, W = shape_latents
-        return torch.randn((1, C, H, W), generator=generator, device=self.sdh.device)
+        return self.dh.get_noise(seed, self.mode)
 
     @torch.no_grad()
     def run_diffusion(
@@ -590,32 +560,41 @@ class LatentBlending():
         """
 
         # Ensure correct num_inference_steps in Holder
-        self.sdh.num_inference_steps = self.num_inference_steps
+        self.dh.set_num_inference_steps(self.num_inference_steps)
         assert type(list_conditionings) is list, "list_conditionings need to be a list"
 
-        if self.mode == 'standard':
+        if self.dh.use_sd_xl:
             text_embeddings = list_conditionings[0]
-            return self.sdh.run_diffusion_standard(
+            return self.dh.run_diffusion_sd_xl(
                 text_embeddings=text_embeddings,
                 latents_start=latents_start,
                 idx_start=idx_start,
                 list_latents_mixing=list_latents_mixing,
                 mixing_coeffs=mixing_coeffs,
-                spatial_mask=self.spatial_mask,
                 return_image=return_image)
 
-        elif self.mode == 'upscale':
-            cond = list_conditionings[0]
-            uc_full = list_conditionings[1]
-            return self.sdh.run_diffusion_upscaling(
-                cond,
-                uc_full,
+        else:
+            text_embeddings = list_conditionings[0]
+            return self.dh.run_diffusion_standard(
+                text_embeddings=text_embeddings,
                 latents_start=latents_start,
                 idx_start=idx_start,
                 list_latents_mixing=list_latents_mixing,
                 mixing_coeffs=mixing_coeffs,
                 return_image=return_image)
 
+        # elif self.mode == 'upscale':
+        #     cond = list_conditionings[0]
+        #     uc_full = list_conditionings[1]
+        #     return self.dh.run_diffusion_upscaling(
+        #         cond,
+        #         uc_full,
+        #         latents_start=latents_start,
+        #         idx_start=idx_start,
+        #         list_latents_mixing=list_latents_mixing,
+        #         mixing_coeffs=mixing_coeffs,
+        #         return_image=return_image)
+
     def run_upscaling(
             self,
             dp_img: str,
@@ -670,8 +649,8 @@ class LatentBlending():
             imgs_lowres.append(Image.open(fp_img_lowres))
 
         # set up upscaling
-        text_embeddingA = self.sdh.get_text_embedding(prompt1)
-        text_embeddingB = self.sdh.get_text_embedding(prompt2)
+        text_embeddingA = self.dh.get_text_embedding(prompt1)
+        text_embeddingB = self.dh.get_text_embedding(prompt2)
         list_fract_mixing = np.linspace(0, 1, nmb_max_branches_lowres - 1)
         for i in range(nmb_max_branches_lowres - 1):
             print(f"Starting movie segment {i+1}/{nmb_max_branches_lowres-1}")
@@ -701,23 +680,35 @@ class LatentBlending():
 
     @torch.no_grad()
     def get_mixed_conditioning(self, fract_mixing):
-        if self.mode == 'standard':
-            text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
+        if self.dh.use_sd_xl:
+            text_embeddings_mix = []
+            for i in range(len(self.text_embedding1)):
+                text_embeddings_mix.append(interpolate_linear(self.text_embedding1[i], self.text_embedding2[i], fract_mixing))
             list_conditionings = [text_embeddings_mix]
-        elif self.mode == 'inpaint':
-            text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
-            list_conditionings = [text_embeddings_mix]
-        elif self.mode == 'upscale':
-            text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
-            cond, uc_full = self.sdh.get_cond_upscaling(self.image1_lowres, text_embeddings_mix, self.noise_level_upscaling)
-            condB, uc_fullB = self.sdh.get_cond_upscaling(self.image2_lowres, text_embeddings_mix, self.noise_level_upscaling)
-            cond['c_concat'][0] = interpolate_spherical(cond['c_concat'][0], condB['c_concat'][0], fract_mixing)
-            uc_full['c_concat'][0] = interpolate_spherical(uc_full['c_concat'][0], uc_fullB['c_concat'][0], fract_mixing)
-            list_conditionings = [cond, uc_full]
         else:
-            raise ValueError(f"mix_conditioning: unknown mode {self.mode}")
+            text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
+            list_conditionings = [text_embeddings_mix]
         return list_conditionings
 
+    # @torch.no_grad()
+    # def get_mixed_conditioning(self, fract_mixing):
+    #     if self.mode == 'standard':
+    #         text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
+    #         list_conditionings = [text_embeddings_mix]
+    #     elif self.mode == 'inpaint':
+    #         text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
+    #         list_conditionings = [text_embeddings_mix]
+    #     elif self.mode == 'upscale':
+    #         text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
+    #         cond, uc_full = self.dh.get_cond_upscaling(self.image1_lowres, text_embeddings_mix, self.noise_level_upscaling)
+    #         condB, uc_fullB = self.dh.get_cond_upscaling(self.image2_lowres, text_embeddings_mix, self.noise_level_upscaling)
+    #         cond['c_concat'][0] = interpolate_spherical(cond['c_concat'][0], condB['c_concat'][0], fract_mixing)
+    #         uc_full['c_concat'][0] = interpolate_spherical(uc_full['c_concat'][0], uc_fullB['c_concat'][0], fract_mixing)
+    #         list_conditionings = [cond, uc_full]
+    #     else:
+    #         raise ValueError(f"mix_conditioning: unknown mode {self.mode}")
+    #     return list_conditionings
+
     @torch.no_grad()
     def get_text_embeddings(
             self,
@@ -729,7 +720,7 @@ class LatentBlending():
             prompt: str
                 ABC trending on artstation painted by Old Greg.
         """
-        return self.sdh.get_text_embedding(prompt)
+        return self.dh.get_text_embedding(prompt)
 
     def write_imgs_transition(self, dp_img):
         r"""
@@ -766,7 +757,7 @@ class LatentBlending():
         # Save as MP4
         if os.path.isfile(fp_movie):
             os.remove(fp_movie)
-        ms = MovieSaver(fp_movie, fps=fps, shape_hw=[self.sdh.height, self.sdh.width])
+        ms = MovieSaver(fp_movie, fps=fps, shape_hw=[self.dh.height_img, self.dh.width_img])
         for img in tqdm(imgs_transition_ext):
             ms.write_frame(img)
         ms.finalize()
@@ -811,7 +802,7 @@ class LatentBlending():
         Set a the seed for a fresh start.
         """
         self.seed = seed
-        self.sdh.seed = seed
+        self.dh.seed = seed
 
     def set_width(self, width):
         r"""
@@ -819,7 +810,7 @@ class LatentBlending():
         """
         assert np.mod(width, 64) == 0, "set_width: value needs to be divisible by 64"
         self.width = width
-        self.sdh.width = width
+        self.dh.width = width
 
     def set_height(self, height):
         r"""
@@ -827,7 +818,7 @@ class LatentBlending():
         """
         assert np.mod(height, 64) == 0, "set_height: value needs to be divisible by 64"
         self.height = height
-        self.sdh.height = height
+        self.dh.height = height
 
     def swap_forward(self):
         r"""

stable_diffusion_holder.py

@@ -155,6 +155,24 @@ class StableDiffusionHolder:
         else:
             self.height = 512
             self.width = 512
+            
+    def get_noise(self, seed, mode='standard'):
+        r"""
+        Helper function to get noise given seed.
+        Args:
+            seed: int
+        """
+        
+        generator = torch.Generator(device=self.device).manual_seed(int(seed))
+        if mode == 'standard':
+            shape_latents = [self.C, self.height // self.f, self.width // self.f]
+            C, H, W = shape_latents
+        elif mode == 'upscale':
+            w = self.image1_lowres.size[0]
+            h = self.image1_lowres.size[1]
+            shape_latents = [self.model.channels, h, w]
+            C, H, W = shape_latents
+        return torch.randn((1, C, H, W), generator=generator, device=self.device)
 
     def set_negative_prompt(self, negative_prompt):
         r"""Set the negative prompt. Currenty only one negative prompt is supported