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https://github.com/Sygil-Dev/sygil-webui.git
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Reverted txt2vid to use the StableDiffusionPipeline instead of StableDiffusionWalkPipeline when loading the model.
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ZeroCool940711
parent
25acf77260
commit
2ea2606e83
@ -377,10 +377,9 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
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if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
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removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
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print(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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print("The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
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text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
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else:
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@ -613,7 +612,7 @@ class StableDiffusionWalkPipeline(DiffusionPipeline):
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def walk(
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self,
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prompts: Optional[List[str]] = None,
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prompt: Optional[List[str]] = None,
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seeds: Optional[List[int]] = None,
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num_interpolation_steps: Optional[Union[int, List[int]]] = 5, # int or list of int
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output_dir: Optional[str] = "./dreams",
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@ -1108,7 +1107,7 @@ def load_diffusers_model(weights_path,torch_device):
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model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-5")
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if not os.path.exists(model_path + "/model_index.json"):
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server_state["pipe"] = StableDiffusionWalkPipeline.from_pretrained(
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server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
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weights_path,
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use_local_file=True,
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use_auth_token=st.session_state["defaults"].general.huggingface_token,
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@ -1116,11 +1115,12 @@ def load_diffusers_model(weights_path,torch_device):
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revision="fp16" if not st.session_state['defaults'].general.no_half else None,
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safety_checker=None, # Very important for videos...lots of false positives while interpolating
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#custom_pipeline="interpolate_stable_diffusion",
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)
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StableDiffusionWalkPipeline.save_pretrained(server_state["pipe"], model_path)
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StableDiffusionPipeline.save_pretrained(server_state["pipe"], model_path)
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else:
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server_state["pipe"] = StableDiffusionWalkPipeline.from_pretrained(
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server_state["pipe"] = StableDiffusionPipeline.from_pretrained(
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model_path,
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use_local_file=True,
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torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
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@ -1436,29 +1436,30 @@ def txt2vid(
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# works correctly generating all frames but do not show the preview image
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# we also do not have control over the generation and cant stop it until the end of it.
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#with torch.autocast("cuda"):
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#print (prompts)
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#video_path = server_state["pipe"].walk(
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#prompts=prompts,
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#seeds=seeds,
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#num_interpolation_steps=num_steps,
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#height=height, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#width=width, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#batch_size=4,
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#fps=30,
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#image_file_ext = ".png",
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#eta = 0.0,
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#output_dir=full_path, # Where images/videos will be saved
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##name='test', # Subdirectory of output_dir where images/videos will be saved
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#guidance_scale=cfg_scale, # Higher adheres to prompt more, lower lets model take the wheel
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#num_inference_steps=num_inference_steps, # Number of diffusion steps per image generated. 50 is good default
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#upsample = False,
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##do_loop=do_loop, # Change to True if you want last prompt to loop back to first prompt
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#resume = False,
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#audio_filepath = None,
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#audio_start_sec = None,
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#margin = 1.0,
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#smooth = 0.0,
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#callback=txt2vid_generation_callback, # our callback function will be called with the arguments callback(step, timestep, latents)
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#callback_steps=1 # our callback function will be called once this many steps are processed in a single frame
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#prompt=prompts,
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#seeds=seeds,
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#num_interpolation_steps=num_steps,
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#height=height, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#width=width, # use multiples of 64 if > 512. Multiples of 8 if < 512.
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#batch_size=4,
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#fps=30,
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#image_file_ext = ".png",
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#eta = 0.0,
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#output_dir=full_path, # Where images/videos will be saved
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##name='test', # Subdirectory of output_dir where images/videos will be saved
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#guidance_scale=cfg_scale, # Higher adheres to prompt more, lower lets model take the wheel
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#num_inference_steps=num_inference_steps, # Number of diffusion steps per image generated. 50 is good default
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#upsample = False,
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##do_loop=do_loop, # Change to True if you want last prompt to loop back to first prompt
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#resume = False,
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#audio_filepath = None,
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#audio_start_sec = None,
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#margin = 1.0,
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#smooth = 0.0,
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#callback=txt2vid_generation_callback, # our callback function will be called with the arguments callback(step, timestep, latents)
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#callback_steps=1 # our callback function will be called once this many steps are processed in a single frame
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#)
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# old code
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