controlnet first steps

diffusers_holder.py

@@ -28,7 +28,7 @@ from typing import Optional
 from torch import autocast
 from contextlib import nullcontext
 from utils import interpolate_spherical
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -47,27 +47,25 @@ class DiffusersHolder():
         # Check if valid pipe
         self.pipe = pipe
         self.device = str(pipe._execution_device)
-        self.init_type_pipe()
+        self.init_types()
-        self.init_dtype()
         
         self.width_latent = self.pipe.unet.config.sample_size
         self.height_latent = self.pipe.unet.config.sample_size
         
         
-
+    def init_types(self):
-    def init_type_pipe(self):
+        assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
-        self.type_pipe = "StableDiffusionXLPipeline"
+        assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
-        if self.type_pipe == "StableDiffusionXLPipeline":
+        if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
             self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
             self.use_sd_xl = True
+            prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
         else:
             self.use_sd_xl = False
-
+            prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
-    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:
@@ -102,6 +100,7 @@ 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):
         if self.use_sd_xl:
             pr_encoder = self.pipe.encode_prompt
@@ -120,7 +119,6 @@ class DiffusersHolder():
         )
         return prompt_embeds
 
-
     def get_noise(self, seed=420, mode=None):
         H = self.height_latent
         W = self.width_latent
@@ -166,12 +164,28 @@ class DiffusersHolder():
         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(
+    def run_diffusion(
+            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 self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
+            return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
+        elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline':
+            return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
+        elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline':
+            pass
+         
+
+    @torch.no_grad()
+    def run_diffusion_sd12x(
             self,
             text_embeddings: torch.FloatTensor,
             latents_start: torch.FloatTensor,
@@ -204,7 +218,6 @@ class DiffusersHolder():
         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:
@@ -251,25 +264,6 @@ class DiffusersHolder():
             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
@@ -282,7 +276,6 @@ class DiffusersHolder():
         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:
@@ -332,8 +325,6 @@ class DiffusersHolder():
             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()
@@ -374,26 +365,183 @@ class DiffusersHolder():
         else:
             return list_latents_out
 
+    @torch.no_grad()
+    def run_diffusion_controlnet(
+            self,
+            conditioning: list,
+            latents_start: torch.FloatTensor,
+            idx_start: int = 0,
+            list_latents_mixing=None,
+            mixing_coeffs=0.0,
+            return_image: Optional[bool] = False):
+        
+        
+        prompt_embeds = conditioning[0]
+        
+        controlnet = self.pipe.controlnet
+        control_guidance_start = [0.0]
+        control_guidance_end = [1.0]
+        guess_mode = False
+        num_images_per_prompt = 1
+        batch_size = 1
+        eta = 0.0
+        controlnet_conditioning_scale = 1.0
+        image = Image.open("/home/lugo/glif/lora_models/pretrained_model_name_or_path/value_runwayml_stable-diffusion-v1-5_fabian/fabian_in_the_desert/img_001.jpg")
+        
+        # align format for control guidance
+
+        if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
+            control_guidance_start = len(control_guidance_end) * [control_guidance_start]
+        elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
+            control_guidance_end = len(control_guidance_start) * [control_guidance_end]
+
+        # 2. Define call parameters
+
+        device = self.pipe._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = self.guidance_scale > 1.0
+
+        # 4. Prepare image
+        image = self.pipe.prepare_image(
+            image=image,
+            width=None,
+            height=None,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=self.device,
+            dtype=controlnet.dtype,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            guess_mode=guess_mode,
+        )
+        height, width = image.shape[-2:]
+
+        # 5. Prepare timesteps
+        self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
+        timesteps = self.pipe.scheduler.timesteps
+
+        # 6. Prepare latent variables
+        generator = torch.Generator(device=self.device).manual_seed(int(420))
+        latents = latents_start.clone()
+
+        # 7. 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)
+
+        # 7.1 Create tensor stating which controlnets to keep
+        controlnet_keep = []
+        for i in range(len(timesteps)):
+            keeps = [
+                1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
+                for s, e in zip(control_guidance_start, control_guidance_end)
+            ]
+            controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps)
+
+        # 8. Denoising loop
+        for i, t in enumerate(timesteps):
+            # 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)
+
+            control_model_input = latent_model_input
+            controlnet_prompt_embeds = prompt_embeds
+
+            if isinstance(controlnet_keep[i], list):
+                cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
+            else:
+                cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
+
+            down_block_res_samples, mid_block_res_sample = self.pipe.controlnet(
+                control_model_input,
+                t,
+                encoder_hidden_states=controlnet_prompt_embeds,
+                controlnet_cond=image,
+                conditioning_scale=cond_scale,
+                guess_mode=guess_mode,
+                return_dict=False,
+            )
+
+            if guess_mode and do_classifier_free_guidance:
+                # Infered ControlNet only for the conditional batch.
+                # To apply the output of ControlNet to both the unconditional and conditional batches,
+                # add 0 to the unconditional batch to keep it unchanged.
+                down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+            # predict the noise residual
+            noise_pred = self.pipe.unet(
+                latent_model_input,
+                t,
+                encoder_hidden_states=prompt_embeds,
+                cross_attention_kwargs=None,
+                down_block_additional_residuals=down_block_res_samples,
+                mid_block_additional_residual=mid_block_res_sample,
+                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)
+
+            # 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]
+
+        image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
+        image, has_nsfw_concept = self.pipe.run_safety_checker(image, device, prompt_embeds.dtype)
+        image = self.pipe.image_processor.postprocess(image, output_type="pil")
+        return image
+    
+#%%
+
+"""
+steps:
+    x get controlnet vanilla running.
+    - externalize conditions
+    - have conditions as input (use one list)
+    - include latent blending
+    - test latent blending
+    - have lora and latent blending
+    
+"""
 
 
 
 #%%
 
 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')
+    controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16)
-    # xxx
+    pipe = StableDiffusionControlNetPipeline.from_pretrained(
+        "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+    ).to("cuda")
+    
     self = DiffusersHolder(pipe)
-    # xxx
+    
-    self.set_num_inference_steps(50)
+    # get text encoding
-    self.set_dimensions(1536, 1024)
+    
-    prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
+    # get image encoding
-    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])
+    #%% 
+    # # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
+    # pretrained_model_name_or_path = "stabilityai/stable-diffusion-2-1" 
+    # 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(text_embeddings, latents_start)
+    # img_orig = self.latent2image(list_latents_1[-1])
     
     
     
@@ -401,6 +549,7 @@ if __name__ == "__main__":
     
     """
     OPEN
+        - rename text encodings to conditionings
         - other examples
         - kill upscaling? or keep?
         - cleanup