add more misc logging
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408e3d725b
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@ -252,7 +252,8 @@ class ChainPipeline:
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except Exception:
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worker.retries = worker.retries - 1
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logger.exception(
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"error while running stage pipeline, %s retries left", worker.retries
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"error while running stage pipeline, %s retries left",
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worker.retries,
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)
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server.cache.clear()
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run_gc([worker.get_device()])
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@ -28,6 +28,10 @@ class PersistDiskStage(BaseStage):
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stage_source: Optional[Image.Image] = None,
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**kwargs,
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) -> List[Image.Image]:
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logger.info(
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"persisting images to disk: %s, %s", [s.size for s in sources], output
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)
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for source, name in zip(sources, output):
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dest = save_image(server, name, source, params=params, size=size)
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logger.info("saved image to %s", dest)
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@ -47,7 +47,10 @@ class SourceTxt2ImgStage(BaseStage):
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params = params.with_args(prompt=slice_prompt(params.prompt, prompt_index))
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logger.info(
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"generating image using txt2img, %s steps: %s", params.steps, params.prompt
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"generating image using txt2img, %s steps of %s: %s",
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params.steps,
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params.model,
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params.prompt,
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)
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if len(sources):
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@ -125,6 +128,7 @@ class SourceTxt2ImgStage(BaseStage):
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output = list(sources)
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output.extend(result.images)
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logger.debug("produced %s outputs", len(output))
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return output
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def steps(
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@ -69,7 +69,11 @@ def convert_diffusion_diffusers_xl(
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else:
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pipeline.vae = AutoencoderKL.from_pretrained(vae_path)
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pipeline.save_pretrained(temp_path)
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if path.exists(temp_path):
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logger.debug("torch model already exists for %s: %s", source, temp_path)
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else:
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logger.debug("exporting torch model for %s: %s", source, temp_path)
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pipeline.save_pretrained(temp_path)
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# directory -> onnx using optimum exporters
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main_export(
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@ -264,9 +264,7 @@ def load_pipeline(
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if hasattr(pipe, vae):
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vae_model = getattr(pipe, vae)
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vae_model.set_tiled(tiled=params.tiled_vae)
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vae_model.set_window_size(
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params.vae_tile // 8, params.vae_overlap
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)
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vae_model.set_window_size(params.vae_tile // 8, params.vae_overlap)
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# update panorama params
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if params.is_panorama():
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@ -330,7 +330,11 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
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)
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add_region_embeds.append(
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np.concatenate(
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(region_negative_pooled_prompt_embeds, region_pooled_prompt_embeds), axis=0
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(
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region_negative_pooled_prompt_embeds,
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region_pooled_prompt_embeds,
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),
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axis=0,
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)
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)
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@ -440,7 +444,15 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
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for r in range(len(regions)):
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top, left, bottom, right, mult, prompt = regions[r]
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logger.debug("running region prompt: %s, %s, %s, %s, %s, %s", top, left, bottom, right, mult, prompt)
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logger.debug(
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"running region prompt: %s, %s, %s, %s, %s, %s",
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top,
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left,
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bottom,
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right,
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mult,
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prompt,
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)
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# convert coordinates to latent space
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h_start = top // 8
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@ -476,7 +488,9 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
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# perform guidance
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if do_classifier_free_guidance:
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region_noise_pred_uncond, region_noise_pred_text = np.split(region_noise_pred, 2)
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region_noise_pred_uncond, region_noise_pred_text = np.split(
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region_noise_pred, 2
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)
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region_noise_pred = region_noise_pred_uncond + guidance_scale * (
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region_noise_pred_text - region_noise_pred_uncond
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)
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@ -501,7 +515,9 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
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value[:, :, h_start:h_end, w_start:w_end] = latents_region_denoised
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count[:, :, h_start:h_end, w_start:w_end] = 1
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else:
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value[:, :, h_start:h_end, w_start:w_end] += latents_region_denoised * mult
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value[:, :, h_start:h_end, w_start:w_end] += (
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latents_region_denoised * mult
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)
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count[:, :, h_start:h_end, w_start:w_end] += mult
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# take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113
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@ -228,7 +228,7 @@ def parse_float_group(group: Tuple[str, str]) -> Tuple[str, float]:
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def get_tokens_from_prompt(
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prompt: str,
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pattern: Pattern,
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parser = parse_float_group,
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parser=parse_float_group,
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) -> Tuple[str, List[Tuple[str, float]]]:
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"""
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TODO: replace with Arpeggio
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