controlnet first steps
This commit is contained in:
Johannes Stelzer
parent
76f89cb836
commit
bc5713241f
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@ -28,7 +28,7 @@ from typing import Optional
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from torch import autocast
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from contextlib import nullcontext
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from utils import interpolate_spherical
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from diffusers import DiffusionPipeline
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from diffusers import DiffusionPipeline, StableDiffusionControlNetPipeline, ControlNetModel
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from diffusers.models.attention_processor import (
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AttnProcessor2_0,
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LoRAAttnProcessor2_0,
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@ -47,26 +47,24 @@ class DiffusersHolder():
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# Check if valid pipe
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self.pipe = pipe
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self.device = str(pipe._execution_device)
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self.init_type_pipe()
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self.init_dtype()
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self.init_types()
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self.width_latent = self.pipe.unet.config.sample_size
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self.height_latent = self.pipe.unet.config.sample_size
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def init_type_pipe(self):
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self.type_pipe = "StableDiffusionXLPipeline"
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if self.type_pipe == "StableDiffusionXLPipeline":
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def init_types(self):
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assert hasattr(self.pipe, "__class__"), "No valid diffusers pipeline found."
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assert hasattr(self.pipe.__class__, "__name__"), "No valid diffusers pipeline found."
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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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self.use_sd_xl = True
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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else:
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self.use_sd_xl = False
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prompt_embeds = self.pipe._encode_prompt("test", self.device, 1, True)
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self.dtype = prompt_embeds.dtype
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def init_dtype(self):
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if self.type_pipe == "StableDiffusionXLPipeline":
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prompt_embeds, _, _, _ = self.pipe.encode_prompt("test")
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self.dtype = prompt_embeds.dtype
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def set_num_inference_steps(self, num_inference_steps):
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self.num_inference_steps = num_inference_steps
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@ -102,6 +100,7 @@ class DiffusersHolder():
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if len(self.negative_prompt) > 1:
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self.negative_prompt = [self.negative_prompt[0]]
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def get_text_embedding(self, prompt, do_classifier_free_guidance=True):
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if self.use_sd_xl:
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pr_encoder = self.pipe.encode_prompt
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@ -120,7 +119,6 @@ class DiffusersHolder():
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)
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return prompt_embeds
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def get_noise(self, seed=420, mode=None):
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H = self.height_latent
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W = self.width_latent
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@ -166,12 +164,28 @@ class DiffusersHolder():
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image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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image = self.pipe.image_processor.postprocess(image, output_type="pil", do_denormalize=[True] * image.shape[0])
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return np.asarray(image[0])
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@torch.no_grad()
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def run_diffusion_standard(
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def run_diffusion(
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self,
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text_embeddings: torch.FloatTensor,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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if self.pipe.__class__.__name__ == 'StableDiffusionXLPipeline':
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return self.run_diffusion_sd_xl(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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elif self.pipe.__class__.__name__ == 'StableDiffusionPipeline':
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return self.run_diffusion_sd12x(text_embeddings, latents_start, idx_start, list_latents_mixing, mixing_coeffs, return_image)
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elif self.pipe.__class__.__name__ == 'StableDiffusionControlNetPipeline':
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pass
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@torch.no_grad()
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def run_diffusion_sd12x(
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self,
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text_embeddings: torch.FloatTensor,
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latents_start: torch.FloatTensor,
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@ -204,7 +218,6 @@ class DiffusersHolder():
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latents = latents_start.clone()
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list_latents_out = []
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num_warmup_steps = len(timesteps) - self.num_inference_steps * self.pipe.scheduler.order
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for i, t in enumerate(timesteps):
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# Set the right starting latents
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if i < idx_start:
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@ -251,25 +264,6 @@ class DiffusersHolder():
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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# prompt = "photo of a house"
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# self.num_inference_steps = 50
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# mixing_coeffs= 0.0
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# idx_start= 0
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# latents_start = self.get_noise()
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# text_embeddings = self.pipe.encode_prompt(
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# prompt,
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# self.device,
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# num_images_per_prompt=1,
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# do_classifier_free_guidance=True,
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# negative_prompt="",
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# prompt_embeds=None,
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# negative_prompt_embeds=None,
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# pooled_prompt_embeds=None,
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# negative_pooled_prompt_embeds=None,
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# lora_scale=None,
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# )
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# 0. Default height and width to unet
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original_size = (1024, 1024) # FIXME
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crops_coords_top_left = (0, 0) # FIXME
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@ -282,7 +276,6 @@ class DiffusersHolder():
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 1. Check inputs. Raise error if not correct & 2. Define call parameters
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# FIXME see if check_inputs use
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if type(mixing_coeffs) == float:
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list_mixing_coeffs = (1+self.num_inference_steps) * [mixing_coeffs]
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elif type(mixing_coeffs) == list:
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@ -332,8 +325,6 @@ class DiffusersHolder():
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elif i == idx_start:
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latents = latents_start.clone()
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# Mix latents for crossfeeding
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if i > 0 and list_mixing_coeffs[i] > 0:
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latents_mixtarget = list_latents_mixing[i - 1].clone()
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@ -374,26 +365,183 @@ class DiffusersHolder():
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else:
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return list_latents_out
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@torch.no_grad()
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def run_diffusion_controlnet(
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self,
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conditioning: list,
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latents_start: torch.FloatTensor,
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idx_start: int = 0,
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list_latents_mixing=None,
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mixing_coeffs=0.0,
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return_image: Optional[bool] = False):
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prompt_embeds = conditioning[0]
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controlnet = self.pipe.controlnet
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control_guidance_start = [0.0]
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control_guidance_end = [1.0]
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guess_mode = False
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num_images_per_prompt = 1
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batch_size = 1
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eta = 0.0
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controlnet_conditioning_scale = 1.0
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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")
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# align format for control guidance
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if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
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control_guidance_start = len(control_guidance_end) * [control_guidance_start]
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elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
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control_guidance_end = len(control_guidance_start) * [control_guidance_end]
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# 2. Define call parameters
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device = self.pipe._execution_device
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# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
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# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
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# corresponds to doing no classifier free guidance.
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do_classifier_free_guidance = self.guidance_scale > 1.0
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# 4. Prepare image
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image = self.pipe.prepare_image(
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image=image,
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width=None,
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height=None,
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batch_size=batch_size * num_images_per_prompt,
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num_images_per_prompt=num_images_per_prompt,
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device=self.device,
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dtype=controlnet.dtype,
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do_classifier_free_guidance=do_classifier_free_guidance,
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guess_mode=guess_mode,
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)
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height, width = image.shape[-2:]
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# 5. Prepare timesteps
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self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
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timesteps = self.pipe.scheduler.timesteps
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# 6. Prepare latent variables
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generator = torch.Generator(device=self.device).manual_seed(int(420))
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latents = latents_start.clone()
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# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
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extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
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# 7.1 Create tensor stating which controlnets to keep
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controlnet_keep = []
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for i in range(len(timesteps)):
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keeps = [
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1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
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for s, e in zip(control_guidance_start, control_guidance_end)
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]
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controlnet_keep.append(keeps[0] if len(keeps) == 1 else keeps)
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# 8. Denoising loop
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for i, t in enumerate(timesteps):
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# expand the latents if we are doing classifier free guidance
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latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
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latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
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control_model_input = latent_model_input
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controlnet_prompt_embeds = prompt_embeds
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if isinstance(controlnet_keep[i], list):
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cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
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else:
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cond_scale = controlnet_conditioning_scale * controlnet_keep[i]
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down_block_res_samples, mid_block_res_sample = self.pipe.controlnet(
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control_model_input,
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t,
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encoder_hidden_states=controlnet_prompt_embeds,
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controlnet_cond=image,
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conditioning_scale=cond_scale,
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guess_mode=guess_mode,
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return_dict=False,
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)
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if guess_mode and do_classifier_free_guidance:
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# Infered ControlNet only for the conditional batch.
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# To apply the output of ControlNet to both the unconditional and conditional batches,
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# add 0 to the unconditional batch to keep it unchanged.
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down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
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mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
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# predict the noise residual
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noise_pred = self.pipe.unet(
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latent_model_input,
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t,
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encoder_hidden_states=prompt_embeds,
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cross_attention_kwargs=None,
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down_block_additional_residuals=down_block_res_samples,
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mid_block_additional_residual=mid_block_res_sample,
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return_dict=False,
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)[0]
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# perform guidance
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if do_classifier_free_guidance:
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noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
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noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
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image = self.pipe.vae.decode(latents / self.pipe.vae.config.scaling_factor, return_dict=False)[0]
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image, has_nsfw_concept = self.pipe.run_safety_checker(image, device, prompt_embeds.dtype)
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image = self.pipe.image_processor.postprocess(image, output_type="pil")
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return image
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#%%
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"""
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steps:
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x get controlnet vanilla running.
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- externalize conditions
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- have conditions as input (use one list)
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- include latent blending
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- test latent blending
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- have lora and latent blending
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"""
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#%%
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if __name__ == "__main__":
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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pipe.to('cuda')
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# xxx
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controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16)
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pipe = StableDiffusionControlNetPipeline.from_pretrained(
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"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
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).to("cuda")
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self = DiffusersHolder(pipe)
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# xxx
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self.set_num_inference_steps(50)
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self.set_dimensions(1536, 1024)
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prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
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text_embeddings = self.get_text_embedding(prompt)
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generator = torch.Generator(device=self.device).manual_seed(int(420))
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latents_start = self.get_noise()
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list_latents_1 = self.run_diffusion_sd_xl(text_embeddings, latents_start)
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img_orig = self.latent2image(list_latents_1[-1])
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# get text encoding
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# get image encoding
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#%%
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# # pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-0.9"
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# pretrained_model_name_or_path = "stabilityai/stable-diffusion-2-1"
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# pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16)
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# pipe.to('cuda')
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# # xxx
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# self = DiffusersHolder(pipe)
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# # xxx
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# self.set_num_inference_steps(50)
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# # self.set_dimensions(1536, 1024)
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# prompt = "photo of a beautiful cherry forest covered in white flowers, ambient light, very detailed, magic"
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# text_embeddings = self.get_text_embedding(prompt)
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# generator = torch.Generator(device=self.device).manual_seed(int(420))
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# latents_start = self.get_noise()
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# list_latents_1 = self.run_diffusion(text_embeddings, latents_start)
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# img_orig = self.latent2image(list_latents_1[-1])
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@ -401,6 +549,7 @@ if __name__ == "__main__":
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"""
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OPEN
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- rename text encodings to conditionings
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- other examples
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- kill upscaling? or keep?
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- cleanup
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