fixed diffusion call

diffusers_holder.py

@@ -17,7 +17,7 @@ import torch
 import numpy as np
 import warnings
 
-from typing import Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 from utils import interpolate_spherical
 from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
 from diffusers.models.attention_processor import (
@@ -48,6 +48,8 @@ class DiffusersHolder():
         self.height_latent = self.pipe.unet.config.sample_size
         self.width_img = self.width_latent  * self.pipe.vae_scale_factor
         self.height_img = self.height_latent  * self.pipe.vae_scale_factor
+        
+        self.is_sdxl_turbo = False
 
     def init_types(self):
         assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
@@ -90,27 +92,23 @@ class DiffusersHolder():
         if len(self.negative_prompt) > 1:
             self.negative_prompt = [self.negative_prompt[0]]
 
-    def get_text_embedding(self, prompt, do_classifier_free_guidance=True):
+    def get_text_embedding(self, prompt):
-        if self.use_sd_xl:
+        text_embeddings = self.pipe.encode_prompt(
-            pr_encoder = self.pipe.encode_prompt
-        else:
-            pr_encoder = self.pipe._encode_prompt
-
-        prompt_embeds = pr_encoder(
             prompt=prompt,
-            prompt_2=prompt,
+            prompt_2=None,
-            device=self.device,
+            device=self.pipe._execution_device,
             num_images_per_prompt=1,
-            do_classifier_free_guidance=do_classifier_free_guidance,
+            do_classifier_free_guidance=self.pipe.do_classifier_free_guidance,
             negative_prompt=self.negative_prompt,
-            negative_prompt_2=self.negative_prompt,
+            negative_prompt_2=None,
             prompt_embeds=None,
             negative_prompt_embeds=None,
             pooled_prompt_embeds=None,
+            negative_pooled_prompt_embeds=None,
             lora_scale=None,
-            clip_skip=False,
+            clip_skip=self.pipe.clip_skip,
         )
-        return prompt_embeds
+        return text_embeddings
 
     def get_noise(self, seed=420):
         
@@ -126,16 +124,7 @@ class DiffusersHolder():
         )
         
         return latents
-        
+
-        
-        # 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(
@@ -168,41 +157,6 @@ class DiffusersHolder():
         
         return image
         
-        # if output_type == "np":
-        #     return np.asarray(image)
-        # else:
-        #     return image
-    
-    
-        # # xxx
-        # 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])[0]
-        # if output_type == "np":
-        #     return np.asarray(image)
-        # else:
-        #     return image
 
     def prepare_mixing(self, mixing_coeffs, list_latents_mixing):
         if type(mixing_coeffs) == float:
@@ -438,7 +392,7 @@ class DiffusersHolder():
         
         
     @torch.no_grad()
-    def run_diffusion_sd_xl(
+    def run_diffusion_sd_xl_turbo(
         self,
         text_embeddings: list,
         latents_start: torch.FloatTensor,
@@ -457,14 +411,12 @@ class DiffusersHolder():
         negative_prompt_2 = None
         num_images_per_prompt = 1
         eta = 0.0
-        generator = None
         latents = None
         prompt_embeds = None
         negative_prompt_embeds = None
         pooled_prompt_embeds = None
         negative_pooled_prompt_embeds = None
         ip_adapter_image = None
-        output_type = "pil"
         return_dict = True
         cross_attention_kwargs = None
         guidance_rescale = 0.0
@@ -475,8 +427,6 @@ class DiffusersHolder():
         negative_crops_coords_top_left = (0, 0)
         negative_target_size = None
         clip_skip = None
-        callback_on_step_end = None
-        callback_on_step_end_tensor_inputs = ["latents"]
         
 
         # 0. Default height and width to unet
@@ -561,7 +511,6 @@ class DiffusersHolder():
         num_warmup_steps = max(len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order, 0)
 
 
-
         # 9. Optionally get Guidance Scale Embedding
         timestep_cond = None
         if self.pipe.unet.config.time_cond_proj_dim is not None:
@@ -586,7 +535,202 @@ class DiffusersHolder():
             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 self.pipe.do_classifier_free_guidance else 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}
+            if ip_adapter_image is not None:
+                added_cond_kwargs["image_embeds"] = image_embeds
+                
+            noise_pred = self.pipe.unet(
+                latent_model_input,
+                t,
+                encoder_hidden_states=prompt_embeds,
+                timestep_cond=timestep_cond,
+                cross_attention_kwargs=self.pipe.cross_attention_kwargs,
+                added_cond_kwargs=added_cond_kwargs,
+                return_dict=False,
+            )[0]
+
+            # perform guidance
+            if self.pipe.do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + self.pipe.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            if self.pipe.do_classifier_free_guidance and self.pipe.guidance_rescale > 0.0:
+                # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.pipe.guidance_rescale)
+
+            # 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
+            
+
+
+    @torch.no_grad()
+    def run_diffusion_sd_xl(
+        self,
+        text_embeddings: tuple,
+        latents_start: torch.FloatTensor,
+        idx_start: int = 0,
+        list_latents_mixing=None,
+        mixing_coeffs=0.0,
+        return_image: Optional[bool] = False,
+    ):
+        
+        
+        prompt_2 = None
+        height = None
+        width = None
+        timesteps = None
+        denoising_end = None
+        negative_prompt_2 = None
+        num_images_per_prompt = 1
+        eta = 0.0
+        generator = None
+        latents = None
+        prompt_embeds = None
+        negative_prompt_embeds = None
+        pooled_prompt_embeds = None
+        negative_pooled_prompt_embeds = None
+        ip_adapter_image = None
+        output_type = "pil"
+        return_dict = True
+        cross_attention_kwargs = None
+        guidance_rescale = 0.0
+        original_size = None
+        crops_coords_top_left = (0, 0)
+        target_size = None
+        negative_original_size = None
+        negative_crops_coords_top_left = (0, 0)
+        negative_target_size = None
+        clip_skip = None
+        callback = None
+        callback_on_step_end = None
+        callback_on_step_end_tensor_inputs = ["latents"]
+        # kwargs are additional keyword arguments and don't need a default value set here.
+
+        # 0. Default height and width to unet
+        height = height or self.pipe.default_sample_size * self.pipe.vae_scale_factor
+        width = width or self.pipe.default_sample_size * self.pipe.vae_scale_factor
+
+        original_size = original_size or (height, width)
+        target_size = target_size or (height, width)
+
+        # 1. Check inputs. skipped.
+
+        self.pipe._guidance_scale = self.guidance_scale
+        self.pipe._guidance_rescale = guidance_rescale
+        self.pipe._clip_skip = clip_skip
+        self.pipe._cross_attention_kwargs = cross_attention_kwargs
+        self.pipe._denoising_end = denoising_end
+        self.pipe._interrupt = False
+
+        # 2. Define call parameters
+        list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
+        batch_size = 1
+
+        device = self.pipe._execution_device
+
+        # 3. Encode input prompt
+        lora_scale = None
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = text_embeddings
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.pipe.scheduler, self.num_inference_steps, device, timesteps)
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.pipe.unet.config.in_channels
+        latents = latents_start.clone()
+        list_latents_out = []
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        if self.pipe.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
+
+        add_time_ids = self.pipe._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        if negative_original_size is not None and negative_target_size is not None:
+            negative_add_time_ids = self.pipe._get_add_time_ids(
+                negative_original_size,
+                negative_crops_coords_top_left,
+                negative_target_size,
+                dtype=prompt_embeds.dtype,
+                text_encoder_projection_dim=text_encoder_projection_dim,
+            )
+        else:
+            negative_add_time_ids = add_time_ids
+
+        if self.pipe.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([negative_add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+        if ip_adapter_image is not None:
+            output_hidden_state = False if isinstance(self.pipe.unet.encoder_hid_proj, ImageProjection) else True
+            image_embeds, negative_image_embeds = self.pipe.encode_image(
+                ip_adapter_image, device, num_images_per_prompt, output_hidden_state
+            )
+            if self.pipe.do_classifier_free_guidance:
+                image_embeds = torch.cat([negative_image_embeds, image_embeds])
+                image_embeds = image_embeds.to(device)
+
+        # 8. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.pipe.scheduler.order, 0)
+
+        # 9. Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.pipe.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.pipe.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.pipe.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.pipe.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+            
+        self.pipe._num_timesteps = len(timesteps)
+        for i, t in enumerate(timesteps):
+            # Set the right starting latents
+            # Write latents out and skip
+            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 self.pipe.do_classifier_free_guidance else latents
@@ -618,86 +762,124 @@ class DiffusersHolder():
 
             # 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__":
     from PIL import Image
-    #%% 
     from diffusers import AutoencoderTiny
-    # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
+    pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
-    pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
+    # pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
-    pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
+    pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
-    pipe.to('cuda')    # xxx
+    pipe.to("cuda")
-    
+        #%
-    #%
+    # pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
-    pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
+    # pipe.vae = pipe.vae.cuda()
-    pipe.vae = pipe.vae.cuda()
+    #%% resanity
-    #%%
+    import time
-
-    # # xxx
-    # self.set_dimensions((512, 512))
-    # self.set_num_inference_steps(4)
-    # self.guidance_scale = 2
-    # # self.set_dimensions(1536, 1024)
-    # latents_start = torch.randn((1,4,64//1,64)).half().cuda()
-    # # 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])
-    
-    #%%
-    
     self = DiffusersHolder(pipe)
-    num_inference_steps = 4
+    prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
+    negative_prompt = "blurry, ugly, pale" 
+    num_inference_steps = 30
+    guidance_scale = 4
+    
     self.set_num_inference_steps(num_inference_steps)
-    latents_start = self.get_noise()
+    self.guidance_scale = guidance_scale
-    guidance_scale = 0
-    self.guidance_scale = 0
     
-    #% get embeddings1
+    prefix='full'
-    prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
+    for i in range(10):
-    text_embeddings1 = self.get_text_embedding(prompt1)
+        self.set_negative_prompt(negative_prompt)
-    prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
+        
+        text_embeddings = self.get_text_embedding(prompt1)
+        latents_start = self.get_noise(np.random.randint(111111))
+        
+        t0 = time.time()
     
-    #% get embeddings2
+        # img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0]
-    prompt2 = "Photo of a tree"
+        
-    text_embeddings2 = self.get_text_embedding(prompt2)
+        img_refx = self.run_diffusion_sd_xl_resanity(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True)
-    prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
+        
+        dt_ref = time.time() - t0
+        img_refx.save(f"x_{prefix}_{i}.jpg")
+        
+        
+        
     
-    latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
     
-    img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
+    # xxx
-    img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
+    
+    # self.set_negative_prompt(negative_prompt)
+    # self.set_num_inference_steps(num_inference_steps)
+    # text_embeddings1 = self.get_text_embedding(prompt1)
+    # prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
+    # latents_start = self.get_noise(420)
+    # t0 = time.time()
+    # img_dh = self.run_diffusion_sd_xl_resanity(text_embeddings1, latents_start, idx_start=0, return_image=True)
+    # dt_dh = time.time() - t0
+    
+    
+    """
+    sth bad in call
+    sth bad in cond
+    sth bad in noise
+    """
+    
+    # xxxx
+    # #%%
+    
+    # self = DiffusersHolder(pipe)
+    # num_inference_steps = 4
+    # self.set_num_inference_steps(num_inference_steps)
+    # latents_start = self.get_noise(420)
+    # guidance_scale = 0
+    # self.guidance_scale = 0
+    
+    # #% get embeddings1
+    # prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
+    # text_embeddings1 = self.get_text_embedding(prompt1)
+    # prompt_embeds1, negative_prompt_embeds1, pooled_prompt_embeds1, negative_pooled_prompt_embeds1 = text_embeddings1
+    
+    # #% get embeddings2
+    # prompt2 = "Photo of a tree"
+    # text_embeddings2 = self.get_text_embedding(prompt2)
+    # prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
+    
+    # latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
+    
+    # img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
+    # img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
     
     
     
-    # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
+    # # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
     
     
-    # # check if brings same image if restarted
+    # # # check if brings same image if restarted
-    # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
+    # # img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
     
-    # mix latents
+    # # mix latents
-    #%%
+    # #%%
-    idx_mix = 2
+    # idx_mix = 2
-    fract=0.8
+    # fract=0.8
-    latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract)
+    # latents_start_mixed = interpolate_spherical(latents1[idx_mix-1], latents2[idx_mix-1], fract)
-    prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract)
+    # prompt_embeds = interpolate_spherical(prompt_embeds1, prompt_embeds2, fract)
-    pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract)
+    # pooled_prompt_embeds = interpolate_spherical(pooled_prompt_embeds1, pooled_prompt_embeds2, fract)
-    negative_prompt_embeds = negative_prompt_embeds1
+    # negative_prompt_embeds = negative_prompt_embeds1
-    negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
+    # negative_pooled_prompt_embeds = negative_pooled_prompt_embeds1
-    text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds]
+    # text_embeddings_mix = [prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds]
     
-    self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)
+    # self.run_diffusion_sd_xl(text_embeddings_mix, latents_start_mixed, idx_start=idx_start, return_image=True)