working version for sdxl and sdxl turbo
This commit is contained in:
Johannes Stelzer
parent
df3d2d1543
commit
a7eda4a87d
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@ -780,8 +780,8 @@ class DiffusersHolder():
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if __name__ == "__main__":
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from PIL import Image
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from diffusers import AutoencoderTiny
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pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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# pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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#%
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@ -792,13 +792,13 @@ if __name__ == "__main__":
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self = DiffusersHolder(pipe)
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prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
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negative_prompt = "blurry, ugly, pale"
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num_inference_steps = 30
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guidance_scale = 4
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num_inference_steps = 4
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guidance_scale = 0
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self.set_num_inference_steps(num_inference_steps)
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self.guidance_scale = guidance_scale
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prefix='full'
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prefix='turbo'
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for i in range(10):
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self.set_negative_prompt(negative_prompt)
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@ -809,7 +809,7 @@ if __name__ == "__main__":
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# img_refx = self.pipe(prompt=prompt1, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale)[0]
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img_refx = self.run_diffusion_sd_xl_resanity(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True)
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img_refx = self.run_diffusion_sd_xl(text_embeddings=text_embeddings, latents_start=latents_start, return_image=True)
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dt_ref = time.time() - t0
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img_refx.save(f"x_{prefix}_{i}.jpg")
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@ -830,11 +830,7 @@ if __name__ == "__main__":
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# dt_dh = time.time() - t0
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"""
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sth bad in call
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sth bad in cond
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sth bad in noise
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"""
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# xxxx
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# #%%
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@ -584,99 +584,19 @@ class LatentBlending():
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mixing_coeffs=mixing_coeffs,
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return_image=return_image)
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def run_upscaling(
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self,
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dp_img: str,
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depth_strength: float = 0.65,
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num_inference_steps: int = 100,
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nmb_max_branches_highres: int = 5,
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nmb_max_branches_lowres: int = 6,
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duration_single_segment=3,
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fps=24,
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fixed_seeds: Optional[List[int]] = None):
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r"""
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Runs upscaling with the x4 model. Requires that you run a transition before with a low-res model and save the results using write_imgs_transition.
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Args:
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dp_img: str
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Path to the low-res transition path (as saved in write_imgs_transition)
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depth_strength:
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Determines how deep the first injection will happen.
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Deeper injections will cause (unwanted) formation of new structures,
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more shallow values will go into alpha-blendy land.
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num_inference_steps:
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Number of diffusion steps. Higher values will take more compute time.
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nmb_max_branches_highres: int
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Number of final branches of the upscaling transition pass. Note this is the number
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of branches between each pair of low-res images.
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nmb_max_branches_lowres: int
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Number of input low-res images, subsampling all transition images written in the low-res pass.
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Setting this number lower (e.g. 6) will decrease the compute time but not affect the results too much.
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duration_single_segment: float
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The duration of each high-res movie segment. You will have nmb_max_branches_lowres-1 segments in total.
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fps: float
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frames per second of movie
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fixed_seeds: Optional[List[int)]:
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You can supply two seeds that are used for the first and second keyframe (prompt1 and prompt2).
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Otherwise random seeds will be taken.
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"""
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fp_yml = os.path.join(dp_img, "lowres.yaml")
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fp_movie = os.path.join(dp_img, "movie_highres.mp4")
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ms = MovieSaver(fp_movie, fps=fps)
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assert os.path.isfile(fp_yml), "lowres.yaml does not exist. did you forget run_upscaling_step1?"
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dict_stuff = yml_load(fp_yml)
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# load lowres images
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nmb_images_lowres = dict_stuff['nmb_images']
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prompt1 = dict_stuff['prompt1']
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prompt2 = dict_stuff['prompt2']
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idx_img_lowres = np.round(np.linspace(0, nmb_images_lowres - 1, nmb_max_branches_lowres)).astype(np.int32)
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imgs_lowres = []
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for i in idx_img_lowres:
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fp_img_lowres = os.path.join(dp_img, f"lowres_img_{str(i).zfill(4)}.jpg")
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assert os.path.isfile(fp_img_lowres), f"{fp_img_lowres} does not exist. did you forget run_upscaling_step1?"
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imgs_lowres.append(Image.open(fp_img_lowres))
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# set up upscaling
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text_embeddingA = self.dh.get_text_embedding(prompt1)
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text_embeddingB = self.dh.get_text_embedding(prompt2)
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list_fract_mixing = np.linspace(0, 1, nmb_max_branches_lowres - 1)
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for i in range(nmb_max_branches_lowres - 1):
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print(f"Starting movie segment {i+1}/{nmb_max_branches_lowres-1}")
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self.text_embedding1 = interpolate_linear(text_embeddingA, text_embeddingB, list_fract_mixing[i])
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self.text_embedding2 = interpolate_linear(text_embeddingA, text_embeddingB, 1 - list_fract_mixing[i])
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if i == 0:
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recycle_img1 = False
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else:
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self.swap_forward()
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recycle_img1 = True
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self.set_image1(imgs_lowres[i])
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self.set_image2(imgs_lowres[i + 1])
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list_imgs = self.run_transition(
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recycle_img1=recycle_img1,
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recycle_img2=False,
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num_inference_steps=num_inference_steps,
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depth_strength=depth_strength,
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nmb_max_branches=nmb_max_branches_highres)
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list_imgs_interp = add_frames_linear_interp(list_imgs, fps, duration_single_segment)
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# Save movie frame
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for img in list_imgs_interp:
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ms.write_frame(img)
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ms.finalize()
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@torch.no_grad()
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def get_mixed_conditioning(self, fract_mixing):
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if self.dh.use_sd_xl:
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text_embeddings_mix = []
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for i in range(len(self.text_embedding1)):
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text_embeddings_mix.append(interpolate_linear(self.text_embedding1[i], self.text_embedding2[i], fract_mixing))
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list_conditionings = [text_embeddings_mix]
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else:
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text_embeddings_mix = interpolate_linear(self.text_embedding1, self.text_embedding2, fract_mixing)
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list_conditionings = [text_embeddings_mix]
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text_embeddings_mix = []
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for i in range(len(self.text_embedding1)):
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if self.text_embedding1[i] is None:
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mix = None
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else:
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mix = interpolate_linear(self.text_embedding1[i], self.text_embedding2[i], fract_mixing)
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text_embeddings_mix.append(mix)
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list_conditionings = [text_embeddings_mix]
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return list_conditionings
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@torch.no_grad()
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@ -857,22 +777,30 @@ if __name__ == "__main__":
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from diffusers_holder import DiffusersHolder
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from diffusers import DiffusionPipeline
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from diffusers import AutoencoderTiny
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
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# pretrained_model_name_or_path = "stabilityai/stable-diffusion-xl-base-1.0"
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pretrained_model_name_or_path = "stabilityai/sdxl-turbo"
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pipe = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path, torch_dtype=torch.float16, variant="fp16")
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pipe.to("cuda")
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pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
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pipe.vae = pipe.vae.cuda()
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# pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
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# pipe.vae = pipe.vae.cuda()
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dh = DiffusersHolder(pipe)
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# %% Next let's set up all parameters
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size_output = (512, 512)
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prompt1 = "underwater landscape, fish, und the sea, incredible detail, high resolution"
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# size_output = (1024, 1024)
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prompt1 = "photo of underwater landscape, fish, und the sea, incredible detail, high resolution"
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prompt2 = "rendering of an alien planet, strange plants, strange creatures, surreal"
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negative_prompt = "blurry, ugly, pale" # Optional
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duration_transition = 12 # In seconds
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# Spawn latent blending
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lb = LatentBlending(dh)
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# lb.dh.set_num_inference_steps(num_inference_steps)
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lb.set_guidance_scale(0)
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lb.set_prompt1(prompt1)
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lb.set_prompt2(prompt2)
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lb.set_dimensions(size_output)
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@ -880,7 +808,7 @@ if __name__ == "__main__":
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# Run latent blending
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lb.run_transition(fixed_seeds=[420, 421])
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lb.run_transition(fixed_seeds=[420, 421], t_compute_max_allowed=15)
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# Save movie
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fp_movie = f'test.mp4'
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@ -889,12 +817,3 @@ if __name__ == "__main__":
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#%%
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"""
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checkout good tree for num inference steps
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checkout that good nmb inference step given
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timing1: dt_per_diff rename and fix (first time run is super slow)
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timing2: measure time for decoding
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"""
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